Adaptive data recovery from distorted signals

ABSTRACT

This application presents an adaptive data recovery from distorted signals (ADRDS) of original data symbols from intervals or parameters of tone signals derived from a received OFDM signal, including responding to dynamic distortions introduced to the received OFDM signal by an OFDM transmission channel. Such ADRDS is implemented by converting back the derived intervals or parameters into original data symbols corresponding to distinctive sets of the intervals or parameters which the derived intervals or parameters belong to.

This application is Continuation in Part and claims priority benefits ofU.S. non-provisional application Ser. No. 14/852,937 filed on Sep. 14,2015 and issued as U.S. Pat. No. 9,584,171 on Feb. 28, 2017 wherein theSer. No. 14/852,937 is incorporated herein by reference in its entiretyas if fully set forth herein, wherein:

the Ser. No. 14/852,937 is Continuation and claims priority benefits ofU.S. non-provisional application Ser. No. 14/099,907 filed on Dec. 6,2013 and issued as U.S. Pat. No. 9,136,891 on Sep. 15, 2015.

BACKGROUND OF THE INVENTION 1. Field of the Invention

This application presents methods systems and circuits for Adaptive DataRecovery from Distorted Signals (ADRDS), useful particularly forrecovering data from a received OFDM signal distorted by a transmissionlink, by an adaptive decoding of data symbols from intervals orparameters of tone signals of the received OFDM signal.

This application includes a direct data recovery (DDR) based onreversing transmission channel transfer function, in order to achieve adirect recovery of original data and synchronizing clock from receivedsignals affected by all deterministic and random distortions introducedby the channel.

The DDR can eliminate an intermediate recovery of signal transmittedoriginally from received signal, required in conventional solutionsbefore actual data recovery can be made.

Therefore DDR can prevent signal processing errors added by suchintermediate recovery and reduce power consumption and computingresources required in conventional receivers.

The DDR is applicable to communication channels including Non ReturnZero (NRZ) or Pulse Amplitude Modulation (PAM), OFDMMulti-carrier/Multi-tone, Carrierless Amplitude Phase (CAP), FrequencyModulation (FM), Phase Modulation (PM).

The DDR can be applied in data recovery systems for wireless, optical,or wireline communication and in local or remote measurement systems.

The DDR shall be particularly advantageous in system on chip (SOC)implementations of data recovery systems.

Such DDR solutions were presented in application Ser. No. 13/844,722(issued as U.S. Pat. No. 9,100,165).

The DDR includes utilization of inverse signal transformation (IST)presented in application Ser. No. 13/323,820 (issued as U.S. Pat. No.9,077,315) as comprising further a noise filtering with inversetransformation (NFIT) and phase and frequency recovery techniques (PFRT)described therein by separate subsections taken from their applicationSer. No. 12/047,318 (issued as U.S. Pat. No. 8,284,877) and Ser. No.11/931,026 (issued as U.S. Pat. No. 8,374,075) accordingly.

Some elements of asynchronous data recovery solutions presented earlierby the same applicant in PCT/CA06/001332, can be useful in explaining abackground field to DDR contributions in PAM and coherent opticalcommunication.

2. Background of DDR

2.1 General Background of DDR and IST

Conventional methods and systems for data recovery are directed totransformation of specific received signals into shapes similar to thosetransmitted originally before any decoding of data,

-   as they use fixed data decoding schemes, applicable only to such    similarly shaped signals, in order to decode data encoded originally    in the transmitter.

Such conventional solutions, focused on recovering original signalshapes from specific received signal shapes, can not be effective inreversing dynamic and random signal distortions introduced by datalinks, since:

-   said distortions are projecting said original signal shapes into    received signal subspaces instead of transforming them into said    specific received signals;-   said conventional solutions are not directed to applying varying    data decoding schemes responding to said transfer function of    transmission channel and current characteristics of received signal.

In conditions of constantly growing data rates, data links complexityand spectrum utilization, distortions introduced by transmissionchannels are growing into major parts of signals received from remotesources in electronic environments contaminated highly.

Therefore the conventional methods based on said recovery of originalsignal required by said fixed decoding, become comparable to chasing abutterfly into a route leading it into a fixed net instead of lettingbutterfly to fly freely and moving the net into its path.

The IST is based on a fundamentally different principle of operationthan such conventional systems, because mobile adaptive decoding isapplied directly to said received signal space, distorted by thetransmission channel, instead of applying such fixed decoding to theoriginal signal recovered from said received signal space.

Such IST includes utilizing a relation between a subset of receivedsignal space (comprising a particular received signal) and data encodedoriginally into this signal, wherein such relation includes said inversetransformation of channel function.

Furthermore said conventional data recovery from received signalrequires complex processing for achieving said recovery of original datacarrying signal, wherein such complex processing is applied continuouslyto a waveform of over-sampled received signal.

IST replaces such complex processing of received signal with a directapplication of reference frames to the received signal waveform,

-   wherein said reference frames, representing expected shapes of    received signal intervals, are compared with received signal shapes    in order to identify original signal shapes which these received    signal shapes correspond to.

Still furthermore said reference frames and/or their parameters can bederived by a background processing responding to changes of transmissionchannel which are by many orders slower than changes of transmittedsignal,

-   while said recovery of original signal shape requires a real time    processing responding to the changes of transmitted signal which are    by many orders faster.

Therefore such conventional solutions, spending resources on such “realtime reconstruction” of very fast original signals instead of focusingon said more direct data recovery of original data from said receivedsignal subspaces, can not be efficient in utilizing processing resourcesor minimizing power.

Consequently, conventional data recovery methods and circuits havelimitations causing that only linear time invariant filters (LTIfilters) can be used in majority of serial communication links.

Such LTI approximations impair filtering efficiency of the majority ofthe communication links which are non-linear and time variant and havechanging in time characteristics.

Furthermore due to such limitations of conventional solutions; evenrarely used non-linear and/or adaptive filters using adaptive algorithmsto accommodate changing in time characteristics of transmissionchannels, can accommodate only limited and slowly changing portions ofsignal non-linearity and/or distortion caused by nonlinear and/orchanging in time characteristics of transmission channel.

It is the objective of DDR to alleviate such limitations by enablingmore efficient accommodation of line-load, non-linearity and timevariant quick changes of transmission channel such as those caused bycross-talk and inter-band interference from adjacent transmissionchannels.

The non-provisional patent application U.S. Ser. No. 11/931,026 byBogdan issued as U.S. Pat. No. 8,374,075, introduced utilization ofreference frames for detecting data carrying intervals of receivedsignals named therein as received signal edges.

Later than this 931026, PCT/CA06/001332 by Bogdan (see WO 2007/009266),disclosed improved utilization of such edge detection techniquesincluding a comparison of said received signal with edge masks selectedadaptively. Similar tools can be also utilized in next inventions suchas DDR and ADD presented herein.

However the 001332 still requires said recovery of original datacarrying signal or its data defining parameters which involves morecomplex processing and is much less efficient in reversing distortionsand interferences introduced by the transmission channel.

Therefore DDR contributes the fundamentally different principle ofoperation explained above and further below, in order to enable majorimprovements in signal processing efficiency and accuracy over thoseenabled by the earlier 001332 and the other conventional solutions.

Most of earlier data recovery systems; require phase locking to theoriginal transmitter's clock recovered from the distorted receivedsignal. Such recovery of original clock has to be preceded by recoveringan original shape of received signal, in order to minimize phase lockingerrors caused by signal distortions. Therefore such earlier systemsimplement frequency domain filters for noise reduction in the receivedwaveform, and compensate line loads with a feedback signal connectedfrom a receivers output to an input of the receiver.

Said phase locking eliminates immunity to high frequency phase noiseexceeding bandwidth of receivers PLL.

Said frequency domain filters are inefficient in responding to changinghigh frequency noise and often attenuate high frequency data, whileconventional line load compensation offers only delayed responsesinvolving feedback signals which may compromise accuracy and/orstability of line receivers.

In particular, said frequency domain filters are conventionally used forrecovering shape of original signal from serially transmitted pulses.

Since serially transmitted pulses must have widely variable lengths andfrequencies, such frequency domain filters can not eliminate highfrequency phase jitter and attenuate useful part of signal whilefiltering high frequency noise.

Consequently such-frequency domain filters are inherently inefficientand inaccurate in detecting phase of data carrying signals; whileaccurate and reliable phase detection is becoming essential forefficient modern communication based on NRZ/PAM, or PM overcopper/fiber/wireless links.

Since such modern communication links utilize phases of signaltransitions between limited set of signal levels or amplitudes for dataencoding, said limitations in phase detection accuracy and noisefiltering abilities reduce data rates and/or link lengths.

These earlier systems' limitations were partly addressed by solutionspresented in the 001332, wherein:

-   a received signal is densely over-sampled and phases and amplitudes    of data carrying pulses and phases of their edges are recovered    without causing any signal attenuation;-   and a number of data symbols contained in the pulse is determined by    measuring length of such pulse instead of relying on sampling pulse    amplitude with a phase aligned clock targeting a middle of symbol    time periods.

In addition to the elimination of said phase alignment of a localreceiver clock, the 001332 presents solutions directed to instantcompensation of line load effects and crosstalk noise.

Nevertheless even the 001332, still requires said recovery of originaldata carrying signal or its data defining parameters.

Therefore it still has limited efficiency in inverting signaldistortions introduced by data links, as they can not apply said directdata decoding in order to enable accurate and timely responses to saidfast changes of the data carrying signals of high speed communicationlinks.

2.2 Background of NFIT

The purpose of noise filters is to reconstruct original signal byreduction of received signal components representing noise and/or byenhancement of received signal components representing the originalsignal.

Limitations of conventional noise filtering methods and electroniccircuit technologies cause that only linear time invariant filters (LTIfilters) can be used in majority of serial communication links.

Such LTI approximations impair filtering efficiency of the majority ofthe communication links which are non-linear and time variant and havechanging in time characteristics.

Furthermore due to such limitations of conventional solutions; evenrarely used non-linear and/or adaptive filters using adaptive algorithmsto accommodate changing in time characteristics of transmissionchannels, can accommodate only limited and slowly changing portions ofsignal non-linearity and/or distortion caused by nonlinear and/orchanging in time characteristics of transmission channel.

Frequency sampling filters (FSF) capable of recovering particularsinusoidal tones/sub-bands from a composite signal such as OFDM frame,were known and described as rarely used in the book by Richard G. Lyons;“Understanding Digital Signal Processing”, Second Edition 2004 PrenticeHall.

However such frequency sampling filters and other conventional frequencydomain methods do not have time domain solutions needed to preserve andrecover phase alignments of singular cycles of tones/sub-bands to thecomposite signal frame, wherein such phase alignments carry phases oftones/sub-bands transmitting data encoded originally.

It is the objective of solutions presented herein to alleviate suchlimitations by contributing;

-   accommodation of unlimited non-linearity and time variant quick    changes of transmission channel such as those caused by line load,    cross-talk and inter-band interference from adjacent transmission    channels,-   and said time domain solutions combining signal processing in    frequency domain and in time domain, in order to enable recovery of    phases and amplitudes of singular cycles or half-cycles of data    carrying tones or sub-bands comprised in the composite signal.

SUMMARY

1. Summary of IST

This invention is directed to data recovery by applying an inversetransformation of transmission channel transform function.

IST comprises data recovery from wide variety of transmission channelconfigurations (see FIG. 13A), including:

-   an encoder of original data into transmitted signals and a data link    converting transmitted signals into received signal subspaces    corresponding to the original data,-   wherein this conversion can introduce deterministic or random    distortions and/or internal or external interference to the received    signal subspaces;-   said encoder and said data link and an preprocessor of received    signal subspaces converting them into preprocessed signal subspaces    also corresponding to the original data;-   wherein such preprocessed signal subspaces can be utilized to enable    more efficient inverse transformation algorithms (their utilization    is illustrated further on in systems for data recovery from OFDM    frames shown in FIG. 13B, FIG. 13D, FIG. 16);-   said encoder and said data link and said preprocessor and a    processor of said preprocessed signal subspaces converting them into    signal parameters sub-ranges corresponding to the original data (see    FIG. 13E, FIG. 14).

Consequently the IST comprises said direct data recovery with saidinverse transformation applied to wide variety of signals and theirparameters including:

-   said received signal subspaces,-   said preprocessed signal subspaces,-   and said signal parameters sub-ranges.

Such IST is based on utilizing a relation between said data transmittedoriginally and said received subspaces and/or said preprocessedsubspaces and/or said received parameters sub-ranges.

Such basic relations are derived by a background processor based ontheoretical models of transmission channels and/or training sessionsand/or adaptive analysis of received signal samples supplied by awaveform screening and capturing circuit (WFSC) described further below.

In addition to the derivation of these basic relations said backgroundprocessor, implemented as the programmable control unit (PCU), controlsoperations of:

-   said real time processing of data carrying signals and their    derivatives performed by a synchronous sequential processor (SSP)    implementing data recovery operations,-   and said waveform screening and capturing circuit (WFSC).

The application of the inverse transformation and the basic relationsincludes:

-   outlining said received signal subspaces and/or said preprocessed    signal subspaces and/or said signal parameters sub-ranges as    distinctive sets comprising elements corresponding to the same    specific data transmitted originally;-   and defining assignment of specific transmitted data to specific    said received signal subspaces and/or preprocessed signal subspaces    and/or processed said signal parameters sub-ranges;-   wherein such assignment enables direct identification of transmitted    data based on identification of said received signal subspace or    said preprocessed signal subspace or said signal parameters    sub-range which a received signal or a preprocessed signal or a    signal parameter belongs to.

In addition to producing said received parameters useful for said directidentification of transmitted data, IST includes also processing of saidpreprocessed signal space in order to derive processed signal parametersuseful for characterizing elements of subspaces of received signalsspace or subspaces of preprocessed signal space.

Consequently the IST comprises using such received signal parameters fordifferent purposes described below:

-   selecting a reference frame or frames which intervals of received    signal shall be compared to in order to identify received signal    subspaces comprising these intervals;-   selecting a reference frame or frames which intervals of    preprocessed signal shall be compared to in order to identify    preprocessed signal subspaces comprising these intervals;-   direct identification and recovery of data transmitted originally    from a limited set of sub-ranges of said received parameters.

Such IST comprises both methods described below.

A more direct method includes steps presented below:

-   sets of shapes of received signal intervals, expected when    particular data symbols or data units are transmitted originally,    are identified using theoretical models and/or results of training    session and/or results of an adaptive filter control process;-   a relation between said original data symbols or units and    corresponding to them such expected sets of received interval shapes    (also named contour further on), is preprogrammed as an inverse    transformation of transmission channel with its distortions and    interferences;-   a specific shape (contour) of an interval of received signal is    processed in order to detect which such set of said expected    interval shapes (further named as received signal subspace)    comprises the specific shape of the processed interval;-   said inverse transformation is applied to the set of expected    interval shapes (received signal subspace) comprising said processed    interval shape, in order to recover data transmitted originally.

Another method, which can be more suitable for certain data transmissiontechniques (such as OFDM), includes utilization of said intermediatestep described as:

-   preprocessing of said received signal subspaces in order to recover    its components carrying transmitted data (these components are    comprised in preprocessed subspaces as it is explained further    below),-   before applying steps (described above for the shape of received    signal) to shapes of these components intervals by using expected    sets of shapes of components intervals (instead of using said    expected sets of shapes of received signal intervals);-   applying said inverse transformation to said sets of expected    component shapes (further named as preprocessed signal subspaces)    detected as comprising specific shapes of said components intervals,    in order to recover data transmitted originally.

Said preprocessing of the received subspaces is exemplified by usingfrequency sampling filters for recovering sub-bands or tones from OFDMcomposite signal in the NFIT version of IST and it is described furtheron in NFIT related sections of this application;

-   wherein the half-cycles or cycles of said sub-bands or tones    recovered within the NFIT, are exemplifying said preprocessed    received signal space.

IST method applies an inverse channel transformation, definingrelationship between originally transmitted data and received signalshapes, to a frame of reference, characterizing a set of shapescorresponding to a specific original data symbol or a plurality of suchdata symbols, in order to recover said original data from the receivedsignal shape affected by channels distortions and/or interferences.

Such IST method comprises the steps of:

-   capturing an over-sampled received signal waveform;-   calculating estimates of shapes similarity, between an captured    waveform interval and said reference frame, such as correlation    integrals or deviation integrals;-   identifying a closest reference frame by comparing such correlation    integrals or deviation integrals;-   said recovery of said data symbol or said set of data symbols    transmitted originally, by applying the inverse transformation of    said channel transform function to the closest reference and/or an    estimate of said shape similarity with the closest frame;-   wherein such channel transformation and its inverse transformation    can be derived by using theoretical models and/or results of    training session and/or results of an adaptive filter control    process.

The IST method described above; further comprises the steps of:

-   performing preliminary analysis of said waveform captured in the    receivers channel and/or a waveform captured in a neighbor channel    and/or other measurable interference;-   and using such analysis results for said selection of the expected    closest reference frame or a subset of reference frames expected to    comprise such closest frame;

Such IST comprises the apparatus for and steps of:

-   comparing such captured waveform interval with such mask by    producing an estimate of their shapes similarity, named as proximity    estimate, such as correlation integral or deviation integral between    samples belonging to the waveform interval and their counterparts    belonging to the mask;-   using such proximity estimate to detect, if the set of shapes    characterized by the mask used (also named as reference frame)    corresponds to the captured waveform;-   wherein said inverse transformation of the channel transfer function    is applied to the mask (reference frame) characterizing such    corresponding set of shapes, in order to recover said original data    from received signal subjected to transmission channel distortions    and interferences.

The IST further includes instant accommodation of time variant quicklychanging characteristics of transmission channel, caused byinterferences such as line loads or cross-talk or inter-bandinterference;

-   wherein such DRIT comprises the steps of:-   producing real time evaluations of such instantly changing    interferences by a pre-processing of waveforms produced or affected    by said interfering sources;-   using such real time evaluations for a selection of said mask used    for producing said proximity estimate, wherein the selected mask is    pre-designed to accommodate such instant interferences;-   using such proximity estimate to detect, if the set of shapes    characterized by the mask used corresponds to the captured waveform    subjected to the instant interferences.

IST principles of operation discussed herein enable wide spectrum ofsolutions comprising configurations 1-12 described below.

Examples of such IST solutions are shown in FIG. 13A, FIG. 13B, FIG.13D, FIG. 13E and their relations to components detailed in thesubsection “1. Embodiments of NFIT” of DETAILED DESCRIPTION areindicated in the subsection “2. Embodiments of IST”.

1. A system and a method for data recovery from received signalsubspaces (DRRS).

This is the inverse transformation system & method for recoveringtransmitted data from a signal received from a transmission channelwhich includes a data coding circuit, encoding said transmitted datainto transmitted signal contours defined by transmitted signalparameters such as amplitudes or phases, and a signal transmission link,transforming said transmitted signal contours into received signalsubspaces, wherein this link introduces deterministic or randomdistortions affecting the received signal subspaces; wherein the DRRScomprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to recover said transmitted data    based on identification of received signal subspaces comprising said    received signals;-   comparing a set of samples of an interval of said received signal    with elements of a reference frame related to a particular said    signal subspace,-   wherein said particular signal sub-space corresponds to a particular    said transmitted contour encoding specific transmitted data;-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said signal subspace    which said interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover data carried by said received signal interval.

The DRRS is the simplified version of the DRPS PSP system (described inthe clause 8 below and shown in FIG. 13A), as it is implemented withoutthe preprocessing and processing operations.

Such simplified DRRS applies a predefined reference frame or framesdirectly to the received signals in order to identify received signalssubspaces comprising particular signals.

2. A system and a method for data recovery from received subspaces ofPAM signal (DRRS PAM).

This is the inverse transformation system & method for recoveringtransmitted data from a signal received from a transmission channelwhich includes a data coding circuit for pulse amplitude modulation(PAM), encoding said transmitted data into transmitted signal edgesdefined by transmitted signal parameters such as amplitudes and phasesdetermining amplitudes and lengths of data carrying pulses, and a signaltransmission link, transforming said transmitted edges into receivedsignal subspaces, wherein this link introduces deterministic or randomdistortions affecting the received signal subspaces comprising receivedsignal edges; wherein the DRRS PAM comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to recover said transmitted data    based on identification of said received signal subspaces;-   comparing a set of samples of an interval of said received signal    with elements of a reference frame related to a particular said    signal subspace,-   wherein said particular signal sub-space corresponds to a particular    said transmitted edge encoding specific data transmitted originally;-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said signal subspace    which said interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover data signaled by said received signal interval.

Such DRRS PAM can be useful in less demanding PAM systems with lowernumbers of amplitude levels (such as two or four levels) and/or lowinter-symbol interference (ISI) and/or crosstalk,

-   wherein using a limited number of reference frames applied to the    same signal interval can be sufficient.

3. A system and a method for data recovery from received signalsubspaces using processed signal parameters (DRRS PSP).

This is the inverse transformation system & method for recovering datafrom received signal subspaces produced by a transmission channel whichincludes a data coding circuit, encoding said transmitted data intotransmitted signal contours defined by transmitted signal parameterssuch as amplitudes or phases, and a signal transmission link,transforming said transmitted signal contours into said received signalsubspaces, wherein said link introduces deterministic or randomdistortions affecting said received signal subspaces; wherein the DRRSRSP comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from said received signal subspaces identified as    comprising specific received signals;-   using a preprocessor of said received signals for transforming said    received signal subspaces into preprocessed signal subspaces,-   wherein said preprocessed sub-spaces correspond to said transmitted    contours encoding data transmitted originally;-   processing said preprocessed subspaces in order to produce processed    signal parameters enabling selection of a single reference frame or    multiple reference frames close to these preprocessed subspaces;-   using said processed signal parameters for said selection of said    close reference frame or frames;-   comparing a set of samples of an interval of said received signal    with elements of said selected reference frame,-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said received signal    subspace which said received signal interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover data carried by said received signal interval.

4. A system and a method for data recovery from received subspaces ofPAM signal using processed signal parameters (DRRS RSP PAM).

This is the inverse transformation system & method for recoveringtransmitted data from received signal subspaces produced by atransmission channel which includes a data coding circuit for pulseamplitude modulation (PAM), encoding said transmitted data intotransmitted signal edges defined by transmitted signal parameters suchas amplitudes and phases determining amplitudes and lengths of datacarrying pulses, and a signal transmission link, transforming saidtransmitted signal edges into said received signal subspaces, whereinsaid link introduces deterministic or random distortions affecting saidreceived signal subspaces; wherein the DRRS RSP PAM comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from said received signal subspaces identified as    comprising specific received signals;-   using a preprocessor of said received signals for transforming said    received signal subspaces into preprocessed signal subspaces,-   wherein said preprocessed sub-spaces correspond to said transmitted    edges encoding transmitted data;-   processing said preprocessed subspaces in order to produce processed    signal parameters enabling selection of a single reference frame or    multiple reference frames close to these preprocessed subspaces;-   using said processed signal parameters for said selection of said    close reference frame or frames;-   comparing a set of samples of an interval of said received signal    with elements of said selected reference frame,-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said received signal    subspace which said received signal interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover data carried by said received signal interval.

Such DRRS RSP PAM can be particularly advantageous in more demanding PAMsystems with high data rates and utilizing more than 4 amplitude levels,

-   wherein higher numbers of possible edges, increased also by higher    interference levels, would require excessive numbers of reference    frames to be applied simultaneously if said selection of expected    close reference frames were not applied.

5. A system and a method for data recovery from preprocessed signalsubspaces (DRPS).

This is the inverse transformation system & method for recoveringtransmitted data from a preprocessed signal produced by a transmissionchannel which includes a data coding circuit, encoding said transmitteddata into transmitted signal contours defined by transmitted signalparameters such as amplitudes or phases, and a signal transmission linkand a preprocessor of a received signal, transforming said transmittedsignal contours into pre-processed signal subspaces, wherein said linkintroduces deterministic or random distortions affecting received signalsubspaces and said preprocessor transforms said received signalsubspaces into said preprocessed signal subspaces; wherein the DRPScomprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from specific said preprocessed subspaces    identified as comprising specific said preprocessed signals;-   comparing a set of samples of an interval of said preprocessed    signal with elements of a reference frame related to a particular    preprocessed subspace,-   wherein the particular preprocessed sub-space corresponds to a    particular said transmitted contour encoding a particular said    transmitted data;-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said preprocessed    subspace which said preprocessed signal interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover transmitted data carried by said preprocessed    signal interval.

6. A system and a method for data recovery from preprocessed subspacesof OFDM signal (DRPS OFDM)

This is the inverse transformation system & method for recoveringtransmitted data from a preprocessed signal produced by a transmissionchannel which includes a data coding circuit, encoding said transmitteddata into transmitted OFDM frames defining data carrying tones orsub-bands, and a signal transmission link and a preprocessor of receivedOFDM frames, transforming said transmitted OFDM frames into preprocessedsignal subspaces representing specific OFDM tones or sub-bands, whereinsaid link introduces deterministic or random distortions affectingreceived signal subspaces and said preprocessor transforms said receivedsignal subspaces into said preprocessed signal subspaces; wherein theDRPS OFDM comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from specific said preprocessed subspaces    identified as comprising specific said preprocessed signals;-   comparing a set of samples of an interval of said preprocessed    signal with elements of a reference frame related to a particular    said preprocessed signal subspace,-   wherein this particular reference frame corresponds to a half-cycle    or cycle of a particular said tone or sub-band represented by this    preprocessed signal subspace;-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said preprocessed    subspace which said preprocessed signal interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover transmitted data carried by said preprocessed    signal interval.

Such DRPS OFDM is shown in FIG. 13D and described further in thesubsection “2. Embodiments of IST” relating to NFIT components describedin greater detail in the subsection “1. Embodiments of NFIT”.

7. A system and a method for data recovery from preprocessed subspacesof PAM signal (DRPS PAM).

This is the inverse transformation system & method for recoveringtransmitted data from a preprocessed signal produced by a transmissionchannel which includes a data coding circuit for pulse amplitudemodulation (PAM), encoding said transmitted data into transmitted signaledges defined by transmitted signal parameters such as amplitudes andphases determining amplitudes and lengths of data carrying pulses, and asignal transmission link and a preprocessor of a received signal,transforming said transmitted signal edges into pre-processed signalsubspaces, wherein said link introduces deterministic or randomdistortions affecting received signal subspaces and said preprocessortransforms said received signal subspaces into said preprocessed signalsubspaces; wherein the DRPS PAM comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from specific said preprocessed subspaces    identified as comprising said preprocessed signals;-   comparing a set of samples of an interval of said preprocessed    signal with elements of a reference frame related to a particular    preprocessed subspace,-   wherein the particular preprocessed sub-space corresponds to a    particular said transmitted contour encoding a specific said    transmitted data;-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said preprocessed    subspace which said preprocessed signal interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover data carried by said preprocessed signal interval.

8. A system and a method for data recovery from preprocessed signalsubspaces using processed signal parameters (DRPS PSP).

This is the inverse transformation system & method for recoveringtransmitted data from a preprocessed signal produced by a transmissionchannel which includes a data coding circuit, encoding said transmitteddata into transmitted signal contours defined by transmitted signalparameters such as amplitudes or phases, and a signal transmission linkand a preprocessor of a received signal, transforming edges of saidtransmitted signal into preprocessed signal subspaces, wherein said linkintroduces deterministic or random distortions affecting received signalsubspaces and said preprocessor transforms said received signalsubspaces into said preprocessed signal subspaces; wherein the DRPS PSPcomprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from specific said preprocessed subspaces    identified as comprising specific said preprocessed signals;-   processing said preprocessed subspaces in order to produce processed    signal parameters enabling selection of a single reference frame or    multiple reference frames close to these preprocessed subspaces;-   using said processed signal parameters for said selection of said    close reference frame or frames;-   comparing a set of samples of an interval of said preprocessed    signal with elements of said selected reference frame,-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said preprocessed signal    subspace which said preprocessed signal interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover data carried by said received signal interval.

9. A system and a method for data recovery from preprocessed subspacesof OFDM signal using processed signal parameters (DRPS PSP OFDM).

This is the inverse transformation system & method for recoveringtransmitted data from a preprocessed signal produced by a transmissionchannel which includes a data coding circuit, encoding said transmitteddata into transmitted OFDM frames defining data carrying tones orsub-bands, and a signal transmission link and a preprocessor of receivedOFDM frames, transforming said transmitted OFDM frames into preprocessedsignal subspaces representing specific OFDM tones or sub-bands, whereinsaid link introduces deterministic or random distortions affectingreceived signal subspaces and said preprocessor transforms said receivedsignal subspaces into said preprocessed signal subspaces; wherein theDRPS PSP OFDM comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from specific said preprocessed subspaces    identified as comprising specific said preprocessed signals;-   processing said preprocessed subspaces in order to produce processed    signal parameters enabling selection of a single reference frame or    multiple reference frames close to these preprocessed subspaces;-   using said processed signal parameters for said selection of said    close reference frame or frames;-   comparing a set of samples of an interval of said preprocessed    signal with elements of said selected reference frame,-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said preprocessed signal    subspace which said preprocessed signal interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover data carried by said received signal interval.

Such DRPS PSP OFDM is shown in FIG. 13B and described further in thesubsection “2. Embodiments of IST” relating to NFIT components describedin the subsection “1. Embodiments of NFIT”.

10. A system and a method for data recovery from preprocessed subspacesof PAM signal using processed signal parameters (DRPS PSP PAM).

This is the inverse transformation system & method for recoveringtransmitted data from a preprocessed signal produced by a transmissionchannel which includes a data coding circuit for pulse amplitudemodulation (PAM), encoding said transmitted data into transmitted signaledges defined by transmitted signal parameters such as amplitudes andphases determining amplitudes and lengths of data carrying pulses, and asignal transmission link and a preprocessor of a received signal,transforming said transmitted signal edges into pre-processed signalsubspaces, wherein said link introduces deterministic or randomdistortions affecting received signal subspaces and said preprocessortransforms said received signal subspaces into said preprocessed signalsubspaces; wherein the DRPS PSP PAM comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from specific said preprocessed subspaces    identified as comprising specific said preprocessed signals;-   processing said preprocessed subspaces in order to produce processed    signal parameters enabling selection of a single reference frame or    multiple reference frames close to these preprocessed subspaces;-   using said processed signal parameters for said selection of said    close reference frame or frames;-   comparing a set of samples of an interval of said preprocessed    signal with elements of said selected reference frame,-   using a result of a single said comparison or results of multiple    said comparisons for identifying a specific said preprocessed signal    subspace which said preprocessed signal interval belongs to;-   applying said inverse transformation to the identified subspace in    order to recover data carried by said received signal interval.

11. A system and a method for data recovery from processed signalparameters (DRPP).

This is the inverse transformation system & method for recoveringtransmitted data from processed signal parameters produced by atransmission channel which includes a data coding circuit, encoding saidtransmitted data into transmitted signal contours defined by transmittedsignal parameters such as amplitudes or phases, and a signaltransmission link, transforming said transmitted signal contours intoreceived signal subspaces affected by deterministic or randomdistortions, and a preprocessor of said received signal subspaces,transforming them into preprocessed signal subspaces, and a processor ofsaid preprocessed signal subspaces, calculating processed signalparameters characterizing components of said preprocessed subspaces;wherein the DRPSP comprises the steps of:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from said processed signal parameters;-   comparing a particular said processed signal parameter with    reference or references related to it,-   wherein said related reference or references characterize a    sub-range of said processed signal parameter corresponding to a    specific said transmitted data;-   using a result of a single said comparison or results of multiple    said comparisons for identifying said sub-range of processed signal    parameter;-   applying said inverse transformation to the identified sub-range in    order to recover transmitted data corresponding to it.

12. A system and a method for data recovery from processed parameters ofOFDM signal (DRPP OFDM).

This is the inverse transformation system & method for recoveringtransmitted data from processed signal parameters produced by atransmission channel which includes a data coding circuit, encoding saidtransmitted data into transmitted OFDM frames defining data carryingtones or sub-bands, and a signal transmission link, transforming saidtransmitted OFDM frames into received signal subspaces affected bydeterministic or random distortions, and a preprocessor of said receivedsignal subspaces, transforming them into OFDM tones or sub-bands, and aprocessor of said tones or sub-bands, calculating processed signalparameters characterizing amplitudes and phases of half cycles or cyclesof OFDM tones or sub-bands; wherein the DRPSP OFDM comprises the stepsof:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable recovery of said    transmitted data from said processed signal parameters;-   comparing a particular said processed signal parameter with    reference or references related to it,-   wherein said related reference or references characterize a    sub-range of said processed signal parameter corresponding to a    specific said transmitted data;-   using a result of a single said comparison or results of multiple    said comparisons for identifying said sub-range of processed signal    parameter;-   applying said inverse transformation to the identified sub-range in    order to recover transmitted data corresponding to it.

Such DRPP OFDM is shown in FIG. 13E and described further in thesubsection “2. Embodiments of IST” relating to NFIT components describedin the subsection “1. Embodiments of NFIT”.

2. Summary of DDR

Systems and methods for Direct Data Recovery (DDR) apply tocommunication systems including: a data coding circuit encodingtransmitted data into transmitted signals;

-   a transmission link transforming said transmitted signals into    received signal subspaces wherein specific said received signal    subspaces correspond to specific said transmitted signals,-   wherein the transmission link introduces deterministic or random    distortions affecting said received signal subspaces;-   a processor transforming said received signal subspaces into-   subranges of parameters characterizing received signal wherein    specific said parameter subranges correspond to specific said    transmitted data,-   or referencing subspaces characterizing received signal wherein    specific said referencing subspaces correspond to specific said    transmitted data.

The term sub-ranges/subspaces means that there is a parameter range/areferencing space which contains all said parametersubranges/referencing subspaces accordingly corresponding to a completeexpected set of said transmitted data.

Said sub-ranges consist of parameters expressed by numerical valuescharacterizing particular signal intervals,

Said subspaces consist of signal intervals expressed by sets ofnumerical values defining signals as functions of time.

Consequently, said references/reference frames are expressed bynumerical values/sets of numerical values defining functions of time aswell.

The DDR includes deriving an inverse transformation reversing a transferfunction of said transmission channel, in order to enable said recoveryof said transmitted data from said parameters subranges or referencingsubspaces;

-   wherein such derivation includes producing references/reference    frames defining said parameter subranges/referencing subspaces and    an assignment of said specific transmitted data as corresponding to    said specific parameter subranges/referencing subspaces.

Different methods may be used for said deriving of inversetransformation, such as:

-   using a channel training session for a transmission and analysis of    known data patterns transmitted specifically for channel training    purposes;-   continuous updating of said inverse transformation based on an    analysis of known parts of normally transmitted data;-   continuous updating of said inverse transformation based on a    data-based channel estimation.-   Said known parts of normally transmitted data may include:-   headers in frames or super-frames transmitted over copper or optical    links like Ethernet/Sonet;-   preamble frames occurring in OFDM super-frames transmitted over    copper links like DSL/ADSL/VD SL;-   preamble frames transmitted over wireless OFDM links like    WiLAN/WiMAX or CDMA links

Said data-based channel estimation may be applied to accommodate agradual fading of said received signal with corresponding gradualadjustments of said subranges references used in the comparisonsidentifying said parameter subranges.

The DDR includes adaptive data decoding (ADD) which in addition toconventional reversal of data coding made on a transmit side, performsalso a reversal of received signal distortions introduced by atransmission channel;

-   wherein it is shown below that both reversals are achieved by the    same conversion of a parameter sub-range/referencing subspace    (corresponding to said received signal) into data transmitted    originally.

The DDR includes instant implementation of such ADD step by comparingsaid signal parameter/referencing subspace with saidreferences/reference frames and using result of such comparison foraddressing a Content Addressed Memory (CAM) outputting recovered data.

Such adaptive data decoding includes also a method for adaptive datadecoding from OFDM signal (ADD OFDM) combining a reversal of originaldata coding made on a transmit side with a reversal of received signaltransformation or distortion introduced by a transmission link, by adirect conversion of a parameter of a received signal into an originaldata while the received signal and its parameter remain transformed ordistorted by the transmission channel; wherein the ADD OFDM methodcomprises the steps of:

-   deriving references defining sub-ranges of said parameter    corresponding to said original data and an assignment of said    original data to said sub-ranges,-   wherein integrals of tones amplitude gradients instead of the    integrals of amplitudes are calculated and used as tones parameters;-   processing a particular said received signal in order to derive a    particular said parameter of received signal;-   using said references for identifying a particular said sub-range    comprising said particular parameter of received signal;-   recovering a particular original data corresponding to said    particular received signal based on said assignment of original data    to sub-ranges.

Conventional solutions use fixed decoding relations (reverse to thatapplied in transmitter) for decoding data from signals or parametersused for such decoding.

Therefore substantial processing resources have to be spend oncontinuous filtering or correcting of received signals or theirparameters in order to reverse transmission link distortions before suchfixed decoding relations can be applied to received signals orparameters amended already.

DDR contributes opposite data recovery method, which avoids suchfiltering or correcting of received signals or their parameters byamending said decoding relation applied for data recovery instead.

Such DDR solutions comprise a variety of systems & methods some of whichare described below.

1. The DDR using parameters of received signal (DDR PRS).

The DDR PRS recovers transmitted data from a signal received from atransmission channel which includes a data coding circuit encoding saidtransmitted data into transmitted signals, a transmission linktransforming said transmitted signals into received signal subspaceswherein the transmission link introduces deterministic or randomdistortions affecting said signal subspaces and a processor transformingsaid signal subspaces into subranges of received signal parameterswherein specific said parameter subranges correspond to specific saidtransmitted data; wherein the DDR PRS comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable said recovery of said    transmitted data from said parameters subranges;-   wherein such derivation includes producing references defining said    parameter subranges and an assignment of said specific transmitted    data as corresponding to said specific parameter subranges;-   processing a particular said received signal in order to produce a    particular single or multiple said signal parameters affected by the    distortions introduced by the transmission link;-   using a single or multiple said references for identifying a    particular single or multiple said parameter subranges comprising    said particular single or multiple signal parameters,-   in order to utilize said assignment for recovering a particular said    transmitted data corresponding to the particular said received    signal.

Consequently the last step of DDR PRS performs said ADD function, byreversing both original data coding and signal distortions caused by thetransmission link, with the simple conversion limited to:

-   using predefined references for identifying a parameter subrange    comprising specific said signal parameter,-   and recovering transmitted data based on a predefined assignment of    transmitted data to the identified parameter subrange.

The DDR PRS is exemplified below by presenting its implementations intwo different OFDM receivers.

2. The DDR using parameters of sub-band cycles (DDR PSBC).

The DDR PSBC recovers transmitted data symbols from a signal receivedfrom an OFDM transmission channel which includes a data coding circuitencoding transmitted symbols into transmitted OFDM frames, atransmission link transforming said transmitted frames into subspaces ofreceived OFDM frame wherein the transmission link introducesdeterministic or random distortions affecting said frame subspaces and aprocessor transforming said frame subspaces into subranges ofamplitudes/phases of cycles or half cycles of sub-band signals of saidreceived frame, wherein specific said amplitudes/phases subrangescorrespond to specific said transmitted symbols; wherein the DDR PSBCcomprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable said recovery of said    transmitted symbols from said amplitudes/phases subranges;-   wherein such derivation includes producing references defining said    amplitudes/phases subranges and an assignment of said specific    transmitted symbols as corresponding to said specific    amplitudes/phases subranges;-   processing a particular said received frame in order to estimate    particular said amplitudes/phases of cycles or half cycles affected    by the distortions introduced by the transmission link;-   using said references for identifying particular said    amplitudes/phases subranges comprising the particular    amplitudes/phases of cycles or half cycles,-   in order to utilize said assignment for recovering particular said    transmitted symbols corresponding to the particular said received    frame.

The step of processing said received frame, may include recovery of saidsub-band signals needed to estimate said particular amplitudes/phases ofcycles or half cycles.

The last step of DDR PSBC performs said ADD function, by reversing bothoriginal data coding and signal distortions caused by the transmissionlink, with the simple conversion limited to: using predefined referencesfor identifying amplitudes/phases subranges comprising particular saidamplitudes/phases,

-   and recovering transmitted data based on a predefined assignment of    transmitted data to the identified amplitudes/phases subranges.

3. The DDR using parameters of sub-bands (DDR PSB).

The DDR PSB recovers transmitted data symbols from a signal receivedfrom an OFDM transmission channel which includes a data coding circuitencoding transmitted symbols into transmitted OFDM frames, atransmission link transforming said transmitted frames into subspaces ofreceived OFDM frame wherein the transmission link introducesdeterministic or random distortions affecting said frame subspaces and aprocessor transforming said frame subspaces into subranges ofamplitudes/phases of sub-bands of said received frame, wherein specificsaid amplitudes/phases correspond to specific said transmitted symbols;wherein the DDR PSB comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable said recovery of said    transmitted symbols from said amplitudes/phases subranges;-   wherein such derivation includes producing references defining said    amplitudes/phases subranges and an assignment of said specific    transmitted symbols as corresponding to said specific    amplitudes/phases subranges;-   processing a particular said received frame in order to estimate    particular said amplitudes/phases affected by the distortions    introduced by the transmission link;-   using said references for identifying particular said    amplitudes/phases subranges comprising the particular    amplitudes/phases,-   in order to utilize said assignment for recovering particular said    transmitted symbols corresponding to the particular said received    frame.

The step of processing said received frame, may include utilization ofFast Fourier Transfer (FFT) for recovering said amplitudes/phases ofsub-bands of said received frame.

The last step of DDR PSB performs said ADD function, by reversing bothoriginal data coding and signal distortions caused by the transmissionlink, with the simple conversion of:

-   amplitudes/phases subranges comprising said estimates of    amplitudes/phases of received signal sub-bands into data transmitted    originally.

4. The DDR using referencing subspaces (DDR RSS).

The DDR RSS utilizes adaptive data decoding (ADD) which in addition toconventional reversal of data coding made on transmit side, performsalso a reversal of received signal distortions introduced by atransmission channel;

-   wherein it is shown below that both reversals are achieved by the    same conversion of a predefined subspace corresponding to received    signal into data transmitted originally.

The DDR RSS recovers transmitted data from a signal received from atransmission channel which includes a data coding circuit encoding saidtransmitted data into transmitted signals, a transmission linktransforming said transmitted signals into received signal subspaceswherein the transmission link introduces deterministic or randomdistortions affecting said signal subspaces and a processor transformingsaid signal subspaces into data referencing subspaces wherein specificsaid referencing subspaces correspond to specific said transmitted data;wherein the DDR RSS comprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable said recovery of said    transmitted data from said referencing subspaces;-   wherein such derivation includes producing reference frames defining    said referencing subspaces and an assignment of said specific    transmitted data as corresponding to said specific referencing    subspaces;-   processing a particular said received signal in order to transform    it into a particular data referencing signal affected by the    distortions introduced by the transmission link;-   using a single or multiple said reference frames for identifying a    particular referencing subspace comprising said particular    referencing signal,-   in order to utilize said assignment for recovering a particular said    transmitted data corresponding to the particular said received    signal.

The last step of such DDR RSS performs said ADD function, by reversingboth original data coding and signal distortions caused by thetransmission link, with the simple conversion of:

-   said referencing subspace comprising said referencing signal into    data transmitted originally.

The DDR RSS described in the above clause can be exemplified with itsimplementation for OFDM data recovery described below.

5. The DDR using sub-band subspaces (DDR SBS).

The DDR SBS recovers transmitted data symbols from a signal receivedfrom an OFDM transmission channel which includes a data coding circuitencoding transmitted symbols into transmitted OFDM frames, atransmission link transforming said transmitted frames into subspaces ofreceived OFDM frames wherein the transmission link introducesdeterministic or random distortions affecting said frame subspaces and aprocessor transforming said frame subspaces into subspaces of sub-bandsof said received frames, wherein specific said sub-band subspacescorrespond to specific said transmitted symbols; wherein the DDR SBScomprises:

-   deriving an inverse transformation reversing a transfer function of    said transmission channel, in order to enable said recovery of said    transmitted symbols from said sub-band subspaces;-   wherein such derivation includes producing reference frames defining    said sub-band subspaces and an assignment of said specific    transmitted symbols as corresponding to said specific sub-band    subspaces;-   processing a particular said received frame in order to recover this    frame's sub-band signals affected by the distortions introduced by    the transmission link;-   using said reference frames for identifying particular said sub-band    subspaces comprising said recovered sub-band signals,-   in order to utilize said assignment for recovering particular said    transmitted symbols corresponding to the particular said received    frame.

The reference frames used for identifying subspaces comprising saidrecovered sub-band signals may have length limited to cycles of thesesub-band signals.

3. Summary of ADRDS

The ADRDS comprises:

-   1. A method for adaptive data recovery (ADR) from a received    orthogonal frequency division multiplexing (OFDM) signal by an    adaptive decoding of transmitted data symbols from intervals of tone    signals of the received OFDM signal wherein the intervals of the    tone signals correspond to cycles or half-cycles of the tone    signals, wherein the received OFDM signal is produced by a    transmitter coding circuit implementing a transmitter coding of the    transmitted data symbols into a transmitted OFDM signal and a    transmission system performing a transformation of the transmitted    OFDM signal into the received OFDM signal in accordance to a current    transfer function including dynamic distortions introduced to the    received OFDM signal; wherein the ADR method comprises the steps of:-   deriving the current transfer function by processing parts of the    received OFDM signal corresponding to known parts of the transmitted    OFDM signal;-   defining distinctive sets of the intervals of the tone signals by    using a known pattern of the transmitter coding and the current    transfer function of the transmission system, wherein each of the    distinctive sets comprises the intervals of the tone signals    expected to represent one of the transmitted data symbols;-   preloading the transmitted data symbols to a content addressed    memory:-   producing, using a received signal processor, the intervals of the    tone signals from the received OFDM signal;-   identifying ones of the distinctive sets comprising the produced    intervals by processing the produced intervals;-   decoding adaptively the transmitted data symbols by reversing both    the transmitter coding and the current transform function, wherein    the reversing of both the transmitter coding and the current    transfer function is achieved by reading the transmitted data    symbols from the content addressed memory addressed with identifiers    of the identified distinctive sets.-   2. A method for adaptive data recovery (ADR) from a received    orthogonal frequency division multiplexing (OFDM) signal by an    adaptive decoding of transmitted data symbols from parameters of    tone signals of the received OFDM signal, wherein the received OFDM    signal is produced by a transmitter coding circuit implementing a    transmitter coding of the transmitted data symbols into a    transmitted OFDM signal and a transmission system performing a    transformation of the transmitted OFDM signal into the received OFDM    signal in accordance to a current transfer function including    dynamic distortions introduced to the received OFDM signal; wherein    the ADR method comprises the steps of:-   deriving the current transfer function by processing parts of the    received OFDM signal corresponding to known parts of the transmitted    OFDM signal;-   defining distinctive sets of the parameters of the tone signals    wherein each of the distinctive sets comprises the parameters of the    tone signals expected to represent one of the transmitted data    symbols;-   preloading the transmitted data symbols to a content addressed    memory;-   producing, using a real time processor, the parameters of the tone    signals from the received OFDM signal wherein the produced    parameters correspond to amplitudes or phases of the tone signals,    wherein operations of the real time processor are controlled by a    background processor;-   identifying ones of the distinctive sets comprising the produced    parameters by processing the produced parameters;-   decoding adaptively the transmitted data symbols by reversing both    the transmitter coding and the current transform function, wherein    the reversing of both the transmitter coding and the current    transform function is achieved by reading the transmitted data    symbols from the content addressed memory addressed with identifiers    of the identified distinctive sets.-   3. A method for adaptive data recovery (ADR) from a received    orthogonal frequency division multiplexing (OFDM) signal by an    adaptive decoding of transmitted data symbols from parameters of    cycles or half-cycles of tone signals of the received OFDM signal,    wherein the received OFDM signal is produced by a transmitter coding    circuit implementing a transmitter coding of the transmitted data    symbols into a transmitted OFDM signal and a transmission system    performing a transformation of the transmitted OFDM signal into the    received OFDM signal in accordance to a current transfer function    including dynamic distortions introduced to the received OFDM    signal; wherein the ADR method comprises the steps of:-   deriving the current transfer function by processing parts of the    received OFDM signal corresponding to known parts of the transmitted    OFDM signal;-   defining distinctive sets of the parameters of the cycles or    half-cycles of the tone signals wherein each of the distinctive sets    comprises the parameters of the cycles or half-cycles of the tone    signals expected to represent one of the transmitted data symbols;-   preloading the transmitted data symbols to a content addressed    memory;-   producing, using a received signal processor, the parameters of the    cycles or half-cycles of the tone signals from the received OFDM    signal wherein the produced parameters correspond to amplitudes or    phases of the cycles or half-cycles of the tone signals;-   identifying ones of the distinctive sets comprising the produced    parameters by processing the produced parameters;-   decoding adaptively the transmitted data symbols by reversing both    the transmitter coding and the current transfer function, wherein    the reversing of both the transmitter coding and the current    transfer function is achieved by reading the transmitted data    symbols from the content addressed memory addressed with identifiers    of the identified distinctive sets.

4. Summary of NFIT

The NFIT alleviates said fundamental deficiencies of conventional noisefilters as it is explained below.

This disclosure comprises a recovery of the originally transmittedsignal by reversing functioning of the transmission channel whichdistorts and introduces interferences to the transmitted signal, whereinsuch reversal is accomplished by applying an inverse transformation to areceived signal distorted by the transmission channel additionallyintroducing transmission link noise and adjacent channels/bandsinterferences.

Since every transmission channel performs some kind of transformation ofa transmitted signals space consisting of originally transmitted shapesinto a received signal space substantially different due to transmissionchannel distortion and interferences, such inverse transformation canprovide effective universal means for noise filtering recovery of theoriginal signal.

Consequently this disclosure includes a method, a system and anapparatus for noise filtering inverse transformation of a receivedsignal (NFIT) which eliminates non-linear and other distortions andinterferences of the transmission channel from the received signal, bycomprising:

-   capturing an over-sampled received signal waveform;-   pre-filtering and/or analyzing such captured waveform in order to    detect which predefined set of waveform shapes includes the analyzed    waveform, wherein such predefined set of shapes is designed as    implementing a known transformation of an originally transmitted    signal;-   a recovery of said originally transmitted signal (freed of    transmitting channel distortions and interferences) by applying the    inverse transformation of said known transformation of the original    signal into the predefined sets of shapes;-   wherein such original signal transformation and its inverse    transformation can be derived as theoretical models and/or results    of training session and/or results of adaptive optimization process.

The NFIT further comprises comparing such captured waveform shape with aframe of reference (named also as mask) characterizing such set ofpredefined shapes in order to verify which set of predefined shapes thecaptured shape shall belong to, wherein:

-   an captured waveform interval is compared with such mask by    producing an estimate of their shapes similarity (named also as    proximity estimate), such as correlation integral or deviation    integral between samples belonging to the waveform and their    counterparts belonging to the mask;-   such estimate of shapes similarity (proximity estimate) is used to    detect, if the set of shapes characterized by the mask used    corresponds to the captured waveform;-   the inverse transformation of the known transmission channel    transformation is applied to the mask characterizing such    corresponding set of shapes, in order to recover the original signal    free of transmission channel distortions and interferences.

Such NFIT further includes an instant accommodation of time variantquickly changing characteristics of transmission channel, caused byinterferences such as line loads or cross-talk or inter-bandinterference; by comprising:

-   producing real time evaluations of such instantly changing    interferences;-   using such real time evaluations for selection of a different frame    of reference accommodating such instant interferences;-   using such selected different frame for said detection of set of    shapes corresponding to the captured waveform affected by the    instant interferences;-   said recovery of the original signal by performing the inverse    transformation of the frame of reference characterizing such    detected set of shapes.

The NFIT includes a method for data recovery from received DMT orMulti-band frames wherein such data recovery method comprises anintegration of frequency domain and time domain signal processingmethods, wherein:

-   a DMT or Multi-band signal is filtered in frequency domain wherein    frequency filters are used to produce filtered signals having known    phase relation to said DMT or Multi-band frame (i.e. signals are    filtered in phase with the frame);-   said time domain time signal processing of such frequency filtered    signals performed in phase with the frame measures amplitudes and/or    phases of singular half-cycles or cycles of DMT or Multi-band tones;-   such measured amplitudes and/or phases are inversely transformed by    reversing their distortions introduced by a transmission channel, of    said DMT or Multi-band composite signal, including distortions of    previous processing stages;-   such inversely transformed amplitudes and phases are used to recover    data symbols originally encoded in the transmitted signal;-   every set of data symbols recovered from amplitudes and/or phases of    a particular tone or band-frequency, is processed using statistical    methods in order to select the most probable symbol.

Such data recovery method includes sensing amplitudes and/or phases ofsignals surrounding said data carrying tone or band signal and reversingdistortions of such data signal caused by said surrounding signals,wherein such data recovery method comprises:

-   detection of amplitudes and/or phases of noise and/or other signals    surrounding such particular data carrying signal in frequency domain    and/or in time domain;-   using such detected amplitudes and/or phases of noise and/or other    surrounding signals for deriving estimates of data signal    distortions introduced by the surrounding noise and/or other    signals;-   using such distortions estimates for performing reverse    transformation of said detected amplitudes and/or phases of the data    carrying signal into the amplitudes and/or phases corresponding to    the data signal transmitted originally.

This disclosure further includes using a synchronous circular processing(SCP) method and apparatus for very fast front-end signal processingperforming real time functions, such as:

-   continuous waveform over-sampling and capturing, said instant    interferences evaluations, waveform interval analysis and comparison    with pre-selected masks and other signal processing in time domain    and/or frequency domain.

The SCP comprises sequentially connected stages fed with digital samplesproduced by A/D converter of incoming wave-form; wherein:

-   such sequential stage comprises a register for storing and/or    processing sequentially multiple wave-form samples;-   such sequential stage register comprises circularly used segments    designated for storing and/or processing of consecutive samples    assigned to consecutive segments of the stage by an index    circulating within a stage segments number;-   such storing and/or processing of consecutive samples in the    segments of the sequential stage is driven by consecutive circular    clocks designated for a particular stage wherein every such    consecutive circular clock is applied to its designated segment at a    time instant occurring periodically within a circulation cycle;-   outputs of a segment or segments of this sequential stage register    can be used by a next sequential stage while other segment or    segments of the first stage are still accepting and/or processing    other samples during the first stage's circular cycle.

Such SCP enables configuration comprising variety of said consecutivestages having widely varying sizes of registers defined by the stagessegments numbers adjusted to accommodate data processing requirementsbetween the consecutive stages.

Consequently; a number of said stage segments can be adjusted freely toaccommodate FIR and/or IIR filters having different orders, and anyrequired extension of processing time can be achieved by adding acorresponding sampling intervals' number by increasing number ofsegments in the stage register.

The SCP further comprises;

-   using such SCP stage for implementing a digital FIR or IIR filter    producing output which maintains a known phase displacement towards    (is in phase with) a composite input signal;-   using such SCP stages for implementing time domain filters producing    outputs which are in phase with the composite input signal;-   and combining such stages implementing such FIR and/or IIR filters    and/or such time domain filters, into a signal processing system    producing multiple outputs which are in phase with the composite    input signal.

This disclosure still further includes using a programmable control unit(PCU) for on-line back-up processing providing very comprehensive andversatile programmable functions such as:

-   controlling operations of the waveform screening and capturing    circuit (WFSC) providing received waveform samples needed for said    received waveform analysis which uses a training session and/or    implements an adaptive noise filtering procedure updating inverse    transfer function while usual received signal filtering is still    taking place uninterrupted;-   calculating and implementing the inverse transformation function    based on received waveform analysis;-   pre-loading said masks used by the SCP;-   controlling all said SCP operations by pre-loading SCP control    registers which define functions performed by SCP.

The NFIT further includes an inverse transformation of band filteredsignal (ITBFS) method, system and apparatus for recovering the originalsignal from a received signal pre-filtered by a band-pass filter whereinthe inverse transformation is applied to such pre-filtered signal inorder to compensate transmission channel changes caused by instantinterferences such as inter-band interferences and/or line loads and/orcross-talk; wherein the ITBFS comprises:

-   using a band-pass filter for producing a pre-filtered received    signal from the captured waveform;-   producing real time evaluations of such instant interferences which    are in-phase with the pre-filtered signal;-   using such real time evaluations for selection of a different frame    of reference accommodating such instant interferences;-   using such selected different frame for said detection of set of    shapes corresponding to the pre-filtered signal affected by the    instant interferences;-   said recovery of the original signal by performing the inverse    transformation of the frame of reference characterizing such    detected set of shapes.

The ITBFS includes said producing of real time evaluations furthercomprising:

-   a time domain processing of the captured waveform and/or other    pre-filtered frequency bands adjacent to the pre-filtered signal    band, wherein such time domain processing of the waveform and/or the    adjacent frequency bands produce results which are in phase with the    pre-filtered signal in order to accomplish in-phase application of    said selected frame of reference to the pre-filtered signal.

The ITBFS further includes:

-   using said SCP for implementing a band-pass filter producing a    pre-filtered received signal from the captured waveform;-   using said SCP for such time domain processing of the captured    waveform and/or other pre-filtered frequency bands adjacent to the    pre-filtered signal band, in order to produce said results being in    phase with the pre-filtered signal.

This disclosure comprises using NFIT and ITBFS for recovering theoriginally transmitted signal in ADSL/VDSL wireline systems; bycomprising the steps of:

-   using said SCP for implementing band-pass filters for pre-filtering    of discrete tone signals from the captured waveform;-   using said SCP for said time domain processing of the captured    waveform and/or such pre-filtered tone signals adjacent to a    specific pre-filtered tone signal, wherein such time domain    processing of the waveform and/or the adjacent pre-filtered tone    signals produce results which are in phase with the specific    pre-filtered tone signal in order to accomplish in-phase application    of said selected frame of reference to the specific pre-filtered    tone signal.-   wherein by applying such steps to every specific discrete tone    signal, all originally transmitted discrete tone signals are    recovered.

Furthermore this disclosure comprises using NFIT and ITBFS forrecovering originally transmitted signal in a wide variety of othercommunication systems as well; including wireless communication systemssuch as Multi-Band systems and WiLAN.

The NFIT further includes inverse transformation of frequency domainrepresentation of received signal (ITFDR) method and system forfiltering out noise from a received signal frequency spectrum and forcorrecting transmission channel transformation by an inversetransformation of the received signal spectrum into an originallytransmitted signal; wherein:

-   such received frequency spectrum is compared with a spectrum mask,    by producing a spectrum proximity estimate such as a correlation    integral or deviation integral between components of the received    frequency spectrum and their counterparts from the spectrum mask;-   such proximity estimate is used to detect, if the spectrum mask    applied represents a noise filtered version of the received    frequency spectrum;-   said inverse transformation of said transmission channel    transformation is applied to the spectrum mask representing such    noise filtered received spectrum, in order to recover the originally    transmitted signal.

Such ITFDR comprises:

-   using a variety of spectrum masks for approximating different said    received spectrums and/or for filtering out different predictable    and/or random noise components;-   using multiple consecutive proximity estimates for identifying such    spectrum mask which is most effective in said approximating of the    received spectrum and in said noise filtering;-   or using proximity estimates providing indication which spectrum    mask shall be applied as the next in order to detect such most    effective spectrum mask;-   wherein the selection of the next applied spectrum mask is    determined by results of real-time in-phase processing of the    received signal and/or received spectrum and/or by previous    proximity estimates.

The ITFDR method and system includes recovering the original signal froma received signal wherein Fast or Discrete Fourier Transform of receivedsignal, in such communication systems as wireless multi-band OFDM(Orthogonal Frequency Division Multiplexing) or wireline ADSL/VDSL DMT,is inversely transformed in order to recover the content of originallytransmitted data carrying signal; wherein the ITFDR comprises:

-   producing real time evaluations of frequency spectrums of major    interferences affecting frequency bands or discrete tones reproduced    by FFT/DFT, wherein such major interferences include said    re-produced by FFT/DFT frequency spectrums influencing themselves    and adjacent frequency bands/tones or other significant    interferences;-   using such real time evaluations for a selection of a spectrum mask    most suitable for accommodating such major interference sources when    it is applied to a specific band/tone reproduced by FFT/DFT;-   wherein a specific band/tone spectrum reproduced by FFT/DFT is    compared with such selected spectrum mask, by producing a spectrum    proximity estimate such as correlation integral or deviation    integral between components of the band/tone spectrum and their    counterparts from the spectrum mask;-   wherein such proximity estimate is used to detect, if the set of    spectrums characterized by the spectrum mask corresponds to the    specific band/tone spectrum;-   the inverse transformation of the known transmission channel    transformation is applied to the spectrum mask characterizing such    corresponding set of spectrums, in order to recover the original    frequency domain signal free of transmission channel interferences;-   wherein by applying such steps to every specific band/tone spectrum,    the original frequency domain signals transmitted over all    bands/tones are recovered.

The ITFDR further comprises:

-   analyzing frequency spectrums produced by the FFT or DFT during a    training session or adaptive control procedure in order to derive    said spectrum masks corresponding to specific data symbols    transmitted over specific frequency bands or discrete tones, wherein    for every specific data symbol variety of different spectrum masks    can be derived wherein such different spectrum masks correspond to    different content of major sources of interference such as adjacent    frequency bands or discrete tones;-   wherein such derivation of the spectrum masks includes derivation of    an inverse transform of transmission channel transformation which    can be used for said recovery of original frequency domain signals    based on an analysis of said corresponding to them frequency    spectrums reproduced by FFT/DFT on the receiver side.

The NFIT introduced with above examples, further includes methods andsystems characterized below.

The NFIT includes inverse transformation of received signal (ITRS)method and system and apparatus, for recovering originally transmittedsignal and for filtering out a transmission channel transformation andnoise by applying an inverse transformation of said received signal intothe original signal; wherein:

-   a representation of said received signal is compared with a mask, by    producing an proximity estimate such as correlation integral or    deviation integral between components of the received signal    representation and their counterparts from the mask;-   such proximity estimate is used to detect, if the mask applied    represents a noise filtered version of the received signal    representation;-   said inverse transformation of said transmission channel    transformation is applied to the mask representing such noise    filtered received signal, in order to recover the originally    transmitted signal.

Said ITRS comprises:

-   using variety of such masks for approximating different    representations of said received signal and/or for filtering out    different predictable and/or random noise components;-   and/or using results of real-time in-phase processing of such    representation of received signal or representations of predictable    interferences, for such selections of next masks applied;-   using multiple consecutive proximity estimates for selecting said    mask which is most effective in said approximating of the received    signal representation and in said noise filtering;-   and/or using a numerical result of a previous proximity estimate for    selecting such most effective mask.

Said ITRS further comprises:

-   using an over-sampled waveform of a time interval of received signal    as said received signal representation;-   or using a frequency spectrum of said time interval of received    signal as said received signal representation.

Said ITRS using the over-sampled interval waveform as signalrepresentation, comprises a preliminary characterization of a shape ofwaveform interval wherein a result of such interval shapecharacterization facilitates selection of an interval mask applied tothe interval waveform in order to find a correct approximation of thereceived signal interval freed of unpredictable noise; wherein such ITRScomprises:

-   evaluation of an averaged peak amplitude of internal pulses    occurring within the interval waveform;-   evaluation of an averaged phase, of periodical edges of such    internal interval pulses, versus a known phase reference;-   using such averaged amplitude and averaged phase for selecting said    interval mask.

The ITRS comprises applying plurality of such masks and analyzing theirproximity estimates in order to optimize selection of a final intervalmask transformed inversely into the original signal; wherein such ITRScomprises:

-   applying said interval masks having different amplitudes and    analyzing their proximity estimates in order to find an interval    mask providing the closest amplitude wise approximation of the    interval waveform;-   and/or applying said interval masks having different phases and    analyzing their proximity estimates in order to find an interval    mask providing the closest phase wise approximation of the interval    waveform;-   inverse transformation of the interval mask, providing such closest    approximation, into the originally transmitted signal.

The ITRS further comprises different application methods of saidinterval masks having different phases versus said received signalwaveform; wherein:

-   plurality of said interval masks, having different phases versus the    same interval waveform, is applied and their proximity estimates are    analyzed in order to find the closest phase wise approximation of    the interval waveform;-   or the same interval mask is applied to plurality of phase shifted    interval waveforms and resulting proximity estimates are analyzed,    in order to find the optimum phase of the interval mask versus the    received signal waveform approximated by that mask.

This disclosure comprises methods, systems and solutions describedbelow.

-   1. A method for noise filtering inverse transformation (I\ FIT),    comprising a recovery of an original signal by reversing functioning    of the transmission channel distorting the original signal, wherein    such reversal is accomplished by applying an inverse transformation    to a received signal wherein such distortion includes transmission    link noise and adjacent channel or band interference; the NFIT    method comprising the steps of:-   capturing an over-sampled waveform of said received signal;-   pre-filtering or analyzing such captured waveform in order to detect    which predefined set of waveform shapes includes pre-filtered or    analyzed waveform, wherein such predefined set of shapes is designed    as implementing a known transformation of the original signal;-   the recovery of said original signal by applying the inverse    transformation of said known transformation of the original signal    into the predefined sets of shapes;-   wherein such original signal transformation and its inverse    transformation can be derived using theoretical models or results of    training session or results of adaptive optimization process.-   2. A method for noise filtering inverse transformation (NFIT),    comprising a recovery of an original signal by reversing functioning    of the transmission channel distorting the original signal, wherein    such reversal is accomplished by applying an inverse transformation    to a received signal wherein such distortion includes transmission    link noise and adjacent channel or band interference; the NFIT    method comprising the steps of:-   capturing an over-sampled waveform of said received signal;-   comparing an interval of the captured waveform with a mask used as    frame of reference characterizing a set of predefined shapes, in    order to verify which set of predefined shapes such waveform    interval corresponds to,-   wherein such comparison includes-   producing a proximity estimate, estimating similarity of their    shapes, such as correlation integral or deviation integral between    samples belonging to the waveform interval and their counterparts    belonging to the mask,-   and using such proximity estimate for verifying if the set of shapes    characterized by the mask used corresponds to the captured waveform    shape;-   applying the inverse transformation of the known transmission    channel transformation to the mask characterizing such corresponding    set of shapes, in order to recover the original signal.-   3. A method for noise filtering inverse transformation (NFIT),    comprising a recovery of an original signal by reversing functioning    of the transmission channel distorting the original signal, wherein    such reversal, accomplished by applying an inverse transformation to    a received signal, includes accommodation of time variant quickly    changing characteristics of transmission channel caused by    interference including line load or cross-talk or inter-band    interference; the NFIT method comprising the steps of:-   capturing an over-sampled waveform of a received signal;-   producing a real time evaluation of such instantly changing    interference;-   using such evaluation for pre-selection of a mask used as frame of    reference characterizing a set of predefined shapes of said received    signal, in order to accommodate such interference;-   comparing an interval of the captured waveform with such    pre-selected mask, in order to verify which set of predefined shapes    such waveform interval corresponds to,-   wherein such comparison includes producing a proximity estimate,    estimating similarity of their shapes, such as correlation integral    or deviation integral between samples belonging to the waveform    interval and their counterparts belonging to the mask;-   said recovery of the original signal by performing the inverse    transformation of the frame of reference characterizing such    detected set of shapes.-   4. A method for data recovery from a composite frame (DRCR)    comprising discrete multiple tones (DMT) or multiple sub-bands (MSB)    wherein such data recovery method comprises combination of frequency    domain and time domain signal processing methods, the DRCR method    comprising the steps of:-   frequency domain filtering of a composite signal carrying such    composite frame wherein frequency filters produce discrete tones or    sub-bands having known phase relation to said DMT or MSB frame i.e.    said frequency filters keep their outputs in phase with the    composite frame;-   said time domain time signal processing of such discrete tones or    sub-bands performed in phase with the composite frame, measures    amplitudes and/or phases of singular half-cycles or cycles of    discrete tones or sub-bands;-   such measured amplitudes and/or phases are inversely transformed by    reversing their distortions introduced by a transmission channel of    said composite signal, including distortions caused by previous    processing stages;-   such inversely transformed amplitudes and phases are used to recover    data symbols originally encoded in the transmitted signal.-   5. A DRCR as described in clause 4, wherein the DRCR method    comprises the step of:-   selecting the most probable symbol by using statistical methods for    processing a set of data symbols recovered from amplitudes and/or    phases of a particular tone or sub-band.-   6. A method for data recovery from a composite signal (DRCS)    comprising discrete multiple tones (DMT) or multiple sub-bands (MSB)    wherein a combination of frequency domain and time domain signal    processing methods is utilized, wherein amplitudes and/or phases of    signals surrounding a particular data carrying tone or sub-band are    sensed and distortions of said particular tone or sub-band are    reversed; the DRCS method comprising the steps of:-   frequency domain filtering of said composite signal producing    discrete tones or sub-bands having known phase relation to said DMT    or MSB frame;-   said time domain signal processing of such discrete tones or    sub-bands measures amplitudes and/or phases of singular half-cycles    or cycles of discrete tones or sub-bands;-   detection of amplitudes and/or phases of noise and/or other signals    surrounding such particular tone or sub-band, performed in frequency    domain and/or in time domain;-   using such detected amplitudes and/or phases for deriving estimates    of distortions introduced by the surrounding noise and/or other    signals to such particular tone or sub-band;-   using such distortions estimates for performing reverse    transformation of said detected amplitudes and/or phases of such    singular half-cycles or cycles of the particular tone or sub-band    into the amplitudes and/or phases corresponding to data transmitted    originally.-   such reversely transformed amplitudes and phases are used to recover    data symbols originally encoded in the transmitted signal.-   7. A synchronous circular processing (SCP) system for a front-end    signal processing performing real time functions including    continuous over-sampling and capturing of a received signal    waveform, and time domain processing of the captured waveform    producing outputs having a known phase displacement towards the    received signal i.e. being in phase with it; the SCP system    comprising:-   sequentially connected stages fed with digital samples produced by    an A/D converter of received wave-form;-   such sequential stage comprises a register for storing and/or    processing sequentially multiple wave-form samples;-   such sequential stage register comprises circularly used segments    designated for storing and/or processing of consecutive samples    assigned to consecutive segments of the stage by an index    circulating within a stage segments number;-   such storing and/or processing of consecutive samples in the    segments of the sequential stage is driven by consecutive circular    clocks designated for the particular stage wherein every such    consecutive circular clock is applied to its designated segment at a    time instant occurring periodically within a circulation cycle;-   outputs of a segment or segments of this sequential stage register    can be used by a next sequential stage while other segment or    segments of the first stage are still accepting and/or processing    other samples during the first stage's circular cycle.-   8. A synchronous circular processing (SCP) system for a front-end    signal processing performing real time functions including    continuous over-sampling and capturing of a received signal    waveform, and time domain processing of the captured waveform    producing outputs having a known phase displacement towards the    received signal i.e. being in phase with it; the SCP system    comprising:-   sequentially connected stages fed with digital samples produced by    an A/D converter of received wave-form;-   such sequential stage comprises a register for storing and/or    processing sequentially multiple wave-form samples;-   such sequential stage register comprises circularly used segments    designated for storing and/or processing of consecutive samples    assigned to consecutive segments of the stage by an index    circulating within a stage segments number;-   such storing and/or processing of consecutive samples in the    segments of the sequential stage is driven by consecutive circular    clocks designated for the particular stage wherein every such    consecutive circular clock is applied to its designated segment at a    time instant occurring periodically within a circulation cycle;-   outputs of a segment or segments of this sequential stage register    can be used by a next sequential stage while other segment or    segments of the first stage are still accepting and/or processing    other samples during the first stage's circular cycle;-   wherein said consecutive stages have varying sizes of registers    defined by the stages segments numbers adjusted to accommodate data    processing requirements between consecutive stages;-   wherein plurality of said stage segments accommodates FIR and/or IIR    filters which may be of different orders, and any required extension    of processing time can be achieved by adding a corresponding    sampling intervals number by increasing number of segments in the    stage register.-   9. A synchronous circular processing (SCP) method for a front-end    signal processing performing real time functions including    continuous over-sampling and capturing of a received signal waveform    and time domain processing of the captured waveform producing    outputs having a known phase displacement towards the received    signal; the SCP method comprising the steps of:-   using sequentially connected stages for storing or processing of    samples produced by A/D converter of received wave-form;-   wherein such sequential stage comprises a register for storing    and/or processing multiple sequential waveform samples;-   wherein such sequential stage register comprises circularly used    segments designated for storing and/or processing of consecutive    samples assigned to consecutive segments of the stage by an index    circulating within a stage segments number;-   driving such storing and/or processing of consecutive samples in the    segments of the sequential stage, by applying consecutive circular    clocks designated for the particular stage wherein every such    consecutive circular clock is applied to its designated segment at a    time instant occurring periodically within a circulation cycle;-   using outputs of a segment or segments of a particular sequential    stage register by a next sequential stage when other segment or    segments of said particular stage are still accepting and/or    processing other samples during said circular cycle of the    particular stage.-   10. A synchronous circular processing (SCP) method for signal    processing implementing digital FIR and/or IIR filter producing    outputs which maintain known phase displacements towards a composite    input signal such as discrete multi-tone (DMT) or multi-sub-band    (MSB) i.e. said outputs are in phase with the composite signal; the    SCP method comprising the steps of:-   using sequentially connected stages for storing or processing of    samples produced by an A/D converter of received wave-form;-   wherein such sequential stage comprises a register for storing    and/or processing multiple sequential samples fed circularly into    consecutive segments of such register by consecutive circular    clocks;-   using outputs of a segment or segments of a particular sequential    stage register by a next sequential stage when other segment or    segments of the particular stage are still accepting and/or    processing other samples during a circular cycle of the particular    stage;-   using such sequential stage for implementing digital FIR or IIR    filter producing output which maintains a known phase displacement    towards said composite signal;-   or using such sequential stage for implementing time domain filters    producing outputs which are in phase with the composite signal.-   11. A method of adaptive synchronous circular processing (ASCP)    combining use of a front end synchronous circular processor (SCP)    performing real time processing of a received signal, with a    programmable control unit (PCU) providing back-up processing    enabling more comprehensive programmable functions; the ASCP method    comprising the steps of:-   using sequentially connected stages for storing or processing of    samples produced by an A/D converter of a received signal wave-form;-   wherein such sequential stage comprises a register for storing    and/or processing multiple sequential samples fed circularly into    consecutive segments of such register by consecutive circular    clocks;-   using outputs of a segment or segments of a particular sequential    stage register by a next sequential stage when other segment or    segments of the particular stage are still accepting and/or    processing other samples during a circular cycle of said particular    stage;-   using said PCU for controlling operations of the received waveform    screening and capturing circuit (WFSC),-   wherein such WFSC supplies said PCU with received waveform samples    needed for a received waveform analysis;-   using said PCU for updating inverse transformation function by    utilizing a training session and/or implementing an adaptive noise    filtering procedure while received signal processing is taking place    uninterrupted;-   using said PCU for calculating and implementing an inverse    transformation function based on said received waveform analysis,    wherein such inverse transformation reverses received signal    distortions introduced by a transmission channel of the received    signal;-   wherein said PCU implements such inverse transformation by    controlling SCP operations by pre-loading SCP control registers    which define functions performed by SCP.-   12. A method of inverse transformation of a band filtered signal    (ITBFS) for a recovery of an original signal by applying an inverse    transformation to a pre-filtered signal, wherein such inverse    transformation includes compensation of transmission channel change    caused by an instant interference; wherein the ITBFS method    comprises the steps of:-   using a band-pass filter for producing a pre-filtered signal from a    captured waveform of a received signal;-   producing a real time evaluation of such instant interference,    wherein this real time evaluation is produced in phase with the    pre-filtered signal as it has a known phase alignment to such    signal;-   using such real time evaluation for selection of frames of reference    accommodating such instant interference,-   using such selected frames of reference for detecting a set of    shapes corresponding to the pre-filtered signal affected by the    instant interference,-   wherein such detection includes comparison of such frames of    reference, characterizing sets of predefined signal shapes, with a    shape of said affected signal;-   said recovery of the original signal by performing the inverse    transformation of the frame of reference which detected such set of    shapes corresponding to the signal affected by interference.-   13. An ITBFS method as described in clause 12, wherein said band    filtered signal is a tone or sub-band and said received signal is a    composite OFDM signal transmitted over a wireline or wireless link    and said original signal is the original tone or sub-band    incorporated into the composite OFDM signal.-   14. A method for reversal of non-linearity (RNL) of amplifier gain    or signal attenuation by applying a polynomial transformation to a    non-linear signal, wherein the RNL method comprises the steps of:    identification of dependency between amplitude of an original signal    and said non-linear signal; using such dependency for defining    polynomial approximation thresholds and their slope coefficients and    their exponents;-   calculating an exponential component for every said threshold    exceeded by a sample of said non-linear signal,-   wherein such exponential component for the maximum exceeded    threshold, is calculated by rising a difference, between the    non-linear signal sample and the maximum threshold, to a power    defined by said exponent for the maximum threshold;-   wherein such exponential component for every other exceeded    threshold, is calculated by rising a difference, between the next    exceeded threshold and such other exceeded threshold, to a power    defined by said exponent for the other exceeded threshold;-   calculating an approximation component for every such approximation    threshold exceeded by said non-linear signal sample, by multiplying    such exponential component by its slope coefficient;-   addition of such approximation components, calculated for    approximation thresholds exceeded by or equal to the non-linear    signal sample;-   wherein by such addition of the approximation components, calculated    for the approximation thresholds exceeded by or equal to the    non-linear signal sample, said non-linearity is reversed.

15. A method for inverse normalization (IN) of OFDM tone or sub-band,comprising reversal of frequency dependent distortion of said tone orsub-band, by applying normalizing coefficients adjusted to a frequencyof this tone or sub-band in order to equalize amplitude and phasedistortions introduced to the tone or sub-band by an OFDM transmissionchannel and a signal processing applied; such IN method comprises thesteps of:

-   identification of the frequency related amplitude or phase    distortion of such tone or sub-band by sampling and analyzing of a    waveform performed by a programmable control unit (PCU); calculating    such normalizing coefficients for the tone or sub-band, performed by    said PCU;

using such normalizing coefficients for equalizing such frequencyrelated distortions, performed by a real-time processing unit (RTP).

16. A method for noise compensation (NC) for a data carrying tone orsub-band of OFDM signal, utilizing evaluation of a noise patternoccurring around this tone or sub-band; wherein such NC method comprisesthe steps of:

-   detecting a noise pattern occurring in frequency domain by using    frequency domain processing such as Frequency Sampling Filter (FSF)    for noise sensing in a frequency spectrum incorporating this tone or    sub-band;-   detecting a noise pattern occurring in time domain by using time    domain processing for noise sensing over time intervals including    this tone or sub-band;-   using a programmable control unit (PCU)-   for analyzing such noise pattern in frequency domain or in time    domain, in order to create a deterministic or random noise model for    such frequency domain or time domain noise pattern,-   and for deriving a noise compensation coefficient by utilizing such    deterministic or random model;-   using such noise compensation coefficient by a Real Time Processor    (RTP) for improving signal to noise ratio in the data carrying tone    or sub-band;-   using the RTP for time domain recovery of data symbols from singular    sinusoidal cycles of the tone or sub-band,-   applying such noise model for estimating probability of symbols    recovered or for dismissing symbols accompanied by high noise levels    close in time;-   using such probability estimates or dismissals of unreliable symbols    for applying statistical methods for a final recovery of an original    data symbol, transmitted by the tone or sub-band, from such    plurality of data symbols recovered from the singular sinusoidal    cycles.

17. A method for time domain data recovery (TDDR) from a tone orsub-band of OFDM composite frame comprising recovery of data symbolsfrom singular sinusoidal cycles or half-cycles of the tone or sub-band;the TDDR method comprising the steps of:

-   measuring an amplitude of a singular half-cycle or cycle by    calculating an integral of amplitude over a half-cycle or cycle    time;-   measuring a phase of the half-cycle or cycle;-   comparing said measured amplitude to predefined amplitude    thresholds, in order to decode an amplitude related factor;

comparing said measured phase to predefined phase thresholds, in orderto decode a phase related factor;

using such amplitude and/or phase related factor for producing anaddress to a content addressed memory (CAM) storing a counter ofhalf-cycles or cycles detecting occurrences of a particular data symbolduring said OFDM composite frame;

-   wherein such CAM can accommodate plurality of such counters of    occurrences of data symbols detected within this tone or sub-band    during the OFDM frame;-   application of statistical methods for selecting data symbol    recovered from this tone or sub-band, by utilizing content of such    counters of data symbols occurrences within this tone or sub-band.

The DDR and IST comprise using:

-   a synchronous sequential processor (SSP) for capturing and real time    processing of an incoming waveform;-   the wave-from screening & capturing circuit (WFSC) for programmable    screening of the over-sampled unfiltered wave-form, and for    capturing screened out wave-form intervals, and for communicating    said captured intervals and other results to the PCU;-   the programmable control unit (PCU) for supporting adaptive noise    filtering, edge detection and mask selection algorithms.

General definition of the SSP is provided below.

The SSP includes real time capturing and processing of in-comingwave-form and a programmable computing unit (PCU) for controlling SSPoperations and supporting adaptive signal analysis algorithms.

Said SSP comprises an over-sampling of incoming wave-form level by usinga locally generated sampling clock and its sub-clocks generated by theoutputs of serially connected gates which the sampling clock ispropagated through. If an active edge of the wave-form is detected bycapturing a change in a wave-form level, the position of the capturedsignal change represents an edge skew between the wave-form edge and anedge of the sampling clock.

In addition to the above wave-form capturing method, the SSP includes 3other methods of the edge skew capturing which are defined below:

-   -   the sampling clock captures the outputs of serially connected        gates which the incoming wave-form is propagated through;    -   the outputs of serially connected gates which the incoming        wave-form is propagated through, provide wave-form sub-clocks        which capture the sampling clock.    -   the incoming wave-form captures the outputs of serially        connected gates which the sampling clock is propagated through;

The above mentioned edge skew capturing methods further include:

-   -   using falling edges of said sub-clocks for driving clock        selectors which select parallel processing phases during which        positive sub-clocks are enabled to perform said edge skew        capturing, or using rising edges of said sub-clocks for driving        selectors which select parallel processing phases during which        negative sub-clocks are enabled to perform said edge skew        capturing;    -   using serially connected clock selectors for enabling        consecutive sub-clocks, in order to assure that consecutive        sub-clocks will target appropriate consecutive bits of        appropriate capture registers.

The SSP invention includes using said serially connected gates:

-   -   as being an open ended delay line;    -   or being connected into a ring oscillator which can be        controlled in a PLL configuration;    -   or being connected into a delay line which can be controlled in        a delay locked loop (DLL) configuration.

Every said edge skew amounts to a fraction of a sampling clock period.

The SSP comprises measuring time intervals between active wave formedges, as being composed of said edge skew of a front edge of theincoming waveform, an integer number of sampling clock periods betweenthe front edge and an end edge, and said edge skew of the end edge ofthe wave-form.

The SSP further comprises a parallel multiphase processing of incomingsignal by assigning consecutive parallel phases for the capturing ofedge skews and/or processing of other incoming wave-form data withclocks which correspond to consecutive sampling clocks.

Consequently the SSP invention comprises using 1 to N parallel phaseswhich are assigned for processing incoming signal data with clockscorresponding to sampling clock periods numbered from 1 to N, as it isfurther described below:

-   -   circuits of phase1 process edge skews or phase skews or other        incoming signal data with a clock which corresponds to the        sampling clock period number 1;    -   circuits of phase2 process edge skews or phase skews or other        incoming signal data with a clock which corresponds to the        sampling clock period number 2;    -   finally circuits of phase N process edge skews or phase skews or        other incoming signal data with a clock which corresponds to the        sampling clock period number N.

Said parallel multiphase processing allows N times longer capturingand/or processing times for said multiphase stages, compared with asingle phase solution.

The SSP includes parallel stage processing of incoming signal byproviding multiple processing stages which are driven by the same clockwhich is applied simultaneously to inputs of output registers of all theparallel stages.

The SSP further comprises a synchronous sequential processing ofincoming signal by using multiple serially connected processing stageswith every stage being fed by data from the previous stage which areclocked-in by a clock which is synchronous with the sampling clock.

Since every consecutive stage is driven by a clock which is synchronousto the same sampling clock, all the stages are driven by clocks whichare mutually synchronous but may have some constant phase displacementsversus each other.

The SSP further comprises:

-   -   merging of processing phases which occurs if multiple parallel        processing phases are merged into a smaller number of parallel        phases or into a single processing phase, when passing from a        one processing stage to a next processing stage;    -   splitting of processing phases which occurs if one processing        phase is split into multiple processing phases or multiple        processing stages are split into even more processing stages,        when passing from a one processing stage to a next processing        stage.

The SSP includes a sequential clock generation (SCG) circuit which usessaid clock selectors and said sub-clocks: to generate SSP clocks whichdrive said parallel phases and said sequential stages, and to generateselector switching signals for said merging and splitting of processingphases.

The SSP includes time sharing of said parallel phases: which is based onassigning a task of processing of a newly began wave-form pulse to anext available parallel processing phase.

The SSP comprises a sequential phase control (SPC) circuit, which usesresults of a wave edge decoding and said SSP clocks, for performing saidtime sharing phase assignments and for further control of operations ofan already assigned phase.

The SSP comprises passing outputs of a one parallel phase to a nextparallel phase, in order to use said passed outputs for processingconducted by a following stage of the next parallel phase.

The outputs passing is performed: by re-timing output register bits ofthe one phase by clocking them into an output register of the nextparallel phase simultaneously with processing results of the nextparallel phase.

The SSP further comprises all the possible combinations of the abovedefined: parallel multiphase processing, parallel stage processing,synchronous sequential processing, merging of processing phases,splitting of processing phases, and outputs passing.

The SSP includes processing stage configurations using selectors,arithmometers, and output registers, which are arranged as it is definedbelow:

-   -   input selectors select constant values or outputs of previous        stages or outputs of parallel stages or an output of the same        stage to provide arithmometer inputs, and arithmometer output is        clocked-in to an output register by a clock which is synchronous        to the sampling clock;    -   multiple arithmometers are fed with constant values or outputs        of previous stages or outputs of parallel stages or an output of        the same stage, and an output selector selects an arithmometer        output to be clocked-in to an output register by a clock        synchronous to the sampling clock;    -   the above defined configuration as being supplemented by using        an output of an output selector of a parallel processing stage        for controlling output selector functions.

Proper arrangements of said parallel and sequential combinations andsaid stages configurations provide real time processing capabilities forvery wide ranges of signal frequencies and enable a wide coverage ofvery diversified application areas.

Summary of the WFSC is provided below.

The wave-form screening and capturing circuits (WFSC) comprises:

-   -   using programmable data masks and programmable control codes for        verifying incoming wave-form captures for compliance or        non-compliance with a pre-programmed screening patterns;    -   buffering captured data for which the pre-programmed compliance        or non-compliance have been detected;    -   counting a number of the above mentioned detections;    -   communicating both the buffered captured data and the number of        detections, to an internal control unit and/or to an external        unit;    -   using programmable time slot selection circuits for selecting a        time interval for which wave-form captures shall be buffered and        communicated to the PCU.

Said PCU comprises implementation of the functions listed below:

-   -   programming of verification functions and patterns for checking        captured wave-forms for compliance or non-compliance with the        patterns;    -   reading verification results and reading captured wave-forms        which correspond to the preprogrammed verification criteria;    -   reading captured wave-forms which can be pre-selected by the PCU        arbitrarily or based on other inputs from the SSP;    -   programming of noise filtering functions and noise filtering        masks for filtering captured wave-forms;    -   reading results of real-time wave-form processing from the SSP,        processing the results and providing control codes and        parameters for further real-time wave-form processing in the        SSP, in accordance with adaptive signal processing algorithms;    -   reading output data from the SSP, interpreting the data, and        communicating the data to external units.

BRIEF DESCRIPTION OF THE DRAWINGS

1. Brief Description of the NFIT Drawings

FIG. 1 shows Block Diagram of Inverse Transformation Method in order tointroduce major sub-systems defined in FIG. 2-FIG. 11.

FIG. 12A & FIG. 12B define Timing Clocks driving the sub-systems shownin FIG. 2-FIG. 11, wherein:

FIG. 12A shows time slots assigned for sub-clocks driving consecutiveprocessing stages,

FIG. 12B shows sub-clocks driving consecutive bits of circularprocessing stages.

Note:

-   -   the time slots define phase displacements assigned to the        sub-clocks; wherein such time slots are filled periodically with        circular sub-clock pulses, as it is required for driving        specific bits of every circuluar register representing a        circular processing stage.

The FIG. 2-FIG. 11 are numbered correspondingly to the flow of processeddata.

All interconnect signals between these figures have unique namesidentifying their sources and destinations explained in the DetailedDescription utilizing the same names.

Inputs supplied from different drawings are connected at the top or leftside and outputs are generated on the bottom due to the top-down orleft-right data flow observed generally.

Clocked circuits like registers or flip-flops are drawn with two timesthicker lines than combinatorial circuits like arithmometers orselectors.

FIG. 2 shows sampling of DMT signal and correction of it'snon-linearity.

FIG. 3 shows comb filtering of DMT signal.

FIG. 4 shows Resonating IIR Filter for 129 Tone (129T/RIF).

FIG. 4A shows Resonating IIR Filter 129.5 SubTone (129.5ST/RIF).

FIG. 5 shows integration & amplitudes registration for half-cycles of129 Tone.

FIG. 5A shows integration & amplitudes registration for half-cycles of129.5 Sub-Tone.

FIG. 6 shows phase capturing and initialization of tone processing for129 Tone/128.5 Sub-Tone/129.5 Sub-Tone, wherein block 8 shows the FrameSamples Counter and MTP Start generator common for Real Time Processorsfor all Tones/Sub-Tones.

FIG. 7 shows retiming & averaging of positive and negative half-cyclesfor 129 Tone/128.5 Sub-Tone/129.5 Sub-Tone.

FIG. 8 shows amplitude & phase normalization for 129 Tone/128.5Sub-Tone/129.5 Sub-Tone.

FIG. 9A-FIG. 9B show accessing noise compensation coefficients for 129Tone.

FIG. 10 shows using these coefficients for compensating expected noisecontributions from the 128.5 Sub-Tone & 129.5 Sub-Tone.

FIG. 11 shows Recovery and Registration of 129T Frame Symbols.

2. Brief Description of the IST Drawings

FIG. 13A shows data recovery from preprocessed signal subspaces by usingprocessed signal parameters.

FIG. 13B shows data recovery from preprocessed subspaces of OFDM signalby using processed signal parameters (DRPS PSP OFDM).

FIG. 13C shows a comparator of signal interval to reference frame (CSR).

FIG. 13D shows data recovery from preprocessed subspaces of OFDM signal(DRPS OFDM).

FIG. 13E shows data recovery from processed parameters of OFDM signal(DRPP OFDM).

3. Brief Description of the DDR Drawings

FIG. 14 shows Direct Data Recovery using parameters of sub-band cycles(DDR PSBC).

FIG. 15 shows Adaptive Data Decoder (ADD) for the DDR PSBC.

FIG. 16 shows Direct Data Recovery using sub-bands subspaces (DDR SBS).

FIG. 17 shows Adaptive Data Decoder (ADD) for the DDR SBS.

FIG. 18 shows Direct Data Recovery using parameters of sub-bands (DDRPSB).

4. Brief Description of the ADD and ADRDS Drawings

FIG. 19 shows calculation and registration of integrals of amplitudegradients for 129 Tone.

FIG. 20 shows selecting reference frames and derivation of DeviationIntegrals.

FIG. 21 shows detections of minimums of Deviation Integrals and theirutilization for data symbols recovery.

FIG. 22 shows Adaptive Data Decoding from intervals of tone signals.

FIG. 23 shows Adaptive Data Decoding from parameters of tone signals.

DETAILED DESCRIPTION 1. Embodiments of NFIT

The Inverse Transformation Method (ITM) is introduced in FIG. 1 asincluding subsystems shown in blocks 1-7.

These subsystems enable an efficient low-power processing of high-speedoversampled data is enabled by implementing real-time processing units(RTPs) which use simplified algorithms based on variable coefficients.

These RTPs are controlled by a Programmable Control Unit (PCU) whichperforms a background processing. This background processing includesimplementing adaptive non-linear algorithms which analyze received linesignal and intermediate processing results, in order to define suchcoefficients and to download them to content addressed memories such asthe Control Register Set for 129 Tone (mentioned further below as129T_CRS occurring in FIG. 4, FIG. 6 and FIG. 7).

These memories are accessed by the RTPs implementing said ITM outlinedin FIG. 1. These RTPs can be implemented as it is detailed below for 129Tone of DMT Frame.

The RTPs include doing basic sorting of recovered symbols (introduced inthe block 7 of FIG. 1 and detailed in FIG. 11) based on symbolsoccurrence frequencies and noise levels in surrounding sub-tones ortones, while the PCU comprises doing further analysis of such sortedsymbols including use of adaptive statistical methods for finalizingselection of most credible symbols.

Said blocks 1-7 are defined in greater detail in FIG. 2-FIG. 11 andcorresponding descriptions, as it is referenced below:

-   block 1 comprising the PAAR Correction, is detailed in FIG. 2 and    described further below;-   block 2 with the diagram of frequency magnitude response of its    frequency sampling filters (shown on the right side) is detailed in    FIG. 3, FIG. 4, FIG. 4A and described further below;-   block 3 with the diagram illustrating detection of amplitudes and    phases of tones & sub-tones (shown on the right side), is detailed    in FIG. 5, FIG. 5A, FIG. 6, FIG. 7 and described further below;-   block 4 is detailed in FIG. 8;-   block 5 is detailed in FIG. 9A-FIG. 9B and FIG. 10;-   blocks 6 and 7 are detailed in FIG. 11.

The embodiments presented herein are based on the assumption listedbelow:

-   -   DMT OFDM Frame has frequency 4 kHz.    -   DMT Frame comprises OFDM Tones numbered from 32 to 255 (such        OFDM Tones have frequencies equal to Tone_NR×4 kHz)    -   The sampling clock 0/Clk (see FIG. 2) is kept in phase with the        DMT Frame, and has sampling frequency 4 kHz×255×16=16.32 Mhz.

The NFIT (see FIG. 1) comprises a correction of Peak to AverageAmplitude Ratio (PAAR), which reverses a non-linear line signaldistortion caused by a gain limitation of line amplification path when acomposition of tones having different frequencies & phases ascends intoextreme amplitude levels.

The PAAR correction is explained below.

For Ys=Modulus(A/D sample), Ylt=Linearity Threshold, Cs=CompensationSlope;

Yc=Corrected Sample Modulus is calculated as Yc=f(Ys) function definedbelow:

If Ys>Ylt; Yc=Ys+Cs (Ys-Ylt)²

else; Yc=Ys.

Since such correcting function Yc=f(Ys) maintains continuity of thederivative of the resulting corrected curve, such transformationmaintains a smooth transition between the non-corrected and correctedregions while it reverses non-linearity occurring originally in thecorrected region due to the gain limitation.

A detailed implementation of such PAAR correction is shown in FIG. 2,wherein the A/D samples are written into the Stage1 of the SynchronousCircular Processor (SCP) comprising A/D_Buffer0/A/D_Buffer1 driven bythe circular sub-clocks 1/Clk0/1/Clk1 accordingly (see FIG. 12Bexplaining circular sub-clocks applications).

Using 2 buffers having separate processing circuits attached enables twotimes longer processing times for calculating DMT0/DMT1 values withreversed effects of the gain limitation.

The Linearity Threshold (LinThr(D:0)) is subtracted from the amplitudeof the attenuated signal sample (i.e. from theModulus(A/D_Buffer(Sign,D:0)) and such subtraction result is squared andadded to the amplitude of the attenuated sample, in order to reversesaid gain attenuation.

Any non-linearity can be reversed smoothly (i.e. without derivativesdiscontinuity) with any accuracy desired by applying polynomialtransformation:Y _(reversed) =C _(s0) Y _(s); if . . . Y _(s)∈(0,Y _(t1)]Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t1))^(e1); if . . . Y_(s)∈(Y _(t1) ,Y _(t2)]Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t1))^(e1) +C _(s2)(Y_(s) −Y _(t2))^(e2); if . . . Y _(s)∈(Y _(t2) ,Y _(t3)]Y _(reversed) =C _(s0) Y _(s) +C _(s1)(Y _(s) −Y _(t1))^(e1) +C _(s2)(Y_(s) −Y _(t2))^(e2) + . . . +C _(sN)(Y _(S) −Y _(tN))^(eN); if . . . Y_(s)∈(Y _(t(N−1)) ,Y _(tN)]

-   wherein; C_(s0), C_(s1), . . . C_(sN) represent slopes of    approximations added at 0, Y_(t1), Y_(t2), . . . Y_(tN)    non-linearity thresholds.

The implementation and equations shown above illustrate a method forreversal of gain non-linearity and/or signal attenuation, wherein suchmethod comprises:

-   identification of dependency between processed signal attenuation    and attenuated signal amplitude;-   defining approximation thresholds and their approximation slopes and    approximation exponents;-   calculating an exponential component for every said approximation    threshold exceeded by an attenuated signal sample, by rising a    difference, between the attenuated sample and its approximation    threshold, to a power defined by its approximation exponent;-   calculating an approximation component for every such approximation    threshold exceeded by an attenuated signal sample, by multiplying    such exponential component by its slope coefficient;-   addition of such approximation component, calculated for the    particular approximation threshold, to the approximation result    comprising previous approximation components calculated for previous    approximation thresholds exceeded by the attenuated signal sample;-   wherein by such addition of the approximation components calculated    for the approximation thresholds exceeded by the distorted and/or    attenuated signal sample, said gain-non-linearity and/or signal    attenuation is reversed.

This disclosure includes an implementation of a Finite Impulse Response(FIR) filter with a circularly driven register (i.e. consecutiveprocessed samples are clocked in circularly into the register) connectedto circuits processing properly delayed samples supplied by theregister. Such register based FIR filter is shown in FIG. 3 wherein theFIR filter is exemplified as the 1-z⁻⁵¹¹ Comb Filter.

The comb filtering based on “1-z⁻⁵¹¹” begins when N+1=512 samplesinitializing a new tone are collected in CFR2(S0:S511), wherein:

-   the first filtered sample S(511) is filtered with the collected    already samples S(0)/S(509) delayed 511/2 times accordingly, in    order to produce the output CFSO(Sign,E:0) fulfilling the difference    equation v(n)=x(n)−r^(N)x(n−N)−r²x(n−2);-   and similarly the second filtered sample S(0) is filtered with the    collected already samples S(1)/S(510) delayed 511/2 times    accordingly, in order to produce the output CFSE(Sign,E:0)    fulfilling the same difference equation.

Said corrected DMT0/DMT1 outputs of the 1^(st) SCP stage are connectedto the Comb Filter Register 2 driven by 512 circular clocks 2/Clk0,2/Clk3, . . . 2Clk511 in order to enable the 1-z⁻⁵¹¹ Comb Filter of512^(th) order implemented by the 2^(nd) SCP stage.

Such comb filter has 511 zeros assigning 511 Sub-Bands which can beproduced by Frequency Sampling Filters constructed by connecting theoutput of such Comb Filter to 511 resonating filters defined by theequations:1/(1−e ^(j2πk/511) z ⁻¹) for k=0,1,2, . . . 510.

Such idea is implemented in more practical way in FIG. 3 where all thedetails are shown and described (such Frequency Sampling Filtering namedas Type IV FSF is explained comprehensively on the pages 311-319 in thebook “Understanding Digital Signal Processing” by Lyons, Ed. 2004”).

Consequently “even zeros” from the range of ˜64 to ˜510 correspond toeven Sub-Bands 64-510 which are considered as facilitating DMT tonesnumbered from 32T to 255T, while “odd zeros” correspond to separatingthem odd Sub-Bands numbered from 63-511 which are considered asfacilitating noise sensing Sub-Tones numbered as 31.5ST, 32.5ST, and33.5ST to 255.5ST.

Such naming convention of the Tones and Sub-Tones is used further on inthis Section text and drawings.

The Comb Filter shown in FIG. 3 uses selection circuits, connected tothe circularly driven Comb Filter Register 2 (CFR2), for producingconsecutive filtered signal samples.

Another possible implementation can use a shifted CFR2 wherein theDMT0/DMT1 signals are clocked into the same segment S0 of the CFR2 andalways the same segments S0/S511 can be used, as providing 511 timesdelay, for producing Comb Filter Output signal.

This disclosure comprises both; the FIR filter, with the circularlydriven filter register, using the selection circuits connected to theregister for supplying consecutive signal samples, and the FIR filter,with the shifted filter register, utilizing the shifting of the filterregister for supplying consecutive signal samples.

This disclosure includes an implementation of an Infinite ImpulseResponse (IIR) filter with a circularly driven filter register (i.econsecutive filtered samples are clocked circularly into the register)supplying IIR processing circuits with properly delayed samples. SuchIIR filter achieves infinite response characteristic by connectingoutputs of such IIR processing circuits back to the inputs of thecircularly driven register.

Said IIR Filter with circularly driven register (see FIG. 4), usesselection circuits, connected to the outputs of the Resonator FilterRegister (129RFR(S0:S3)), for supplying filter processing circuits whichproduce consecutive filtered signal samples written back circularly intoconsecutive samples S0-S3 of 129RFR (S0:S3).

Such circularly driven IIR filter exemplified in FIG. 4, is a resonatingfilter, having idealistic transfer function (F(z)=1/(1−e^(j2π258/511)z⁻¹)) adjusted into the Type IV FSF (explained in the Lyons bookmentioned above) for better stability and performance.

Another possible implementation can use a shifted Resonator FilterRegister (RFR(S0:S3)) wherein the input signal from the previous stageand outputs of the Resonator Filter Register supply filter processingcircuits which produce filtered sample clocked into the same segment S0of the RFR(S0:S3).

This disclosure comprises both; the IIR filter, with the circularlydriven register, using the selection circuits connected to the registeroutputs for supplying consecutive processed samples, and the IIR filter,with the shifted register, using shifted register outputs for supplyingconsecutive processed samples to the filter processing circuits.

The odd/even output of the comb filter CFS0(Sign,E:0)/CFSE(Sign,E:0)re-timed in the Comb Filter Reg.3 (CFR3) produces Resonant FiltersSelected Input (RFSI(S,E:0)) which is connected to the multipleresonating Infinite Impulse Response (IIR) Filters designated forspecific Tones or Sub-Tones.

Such resonating IIR filter designated for the 129Tone (129T) is shown inFIG. 4, wherein:

-   the reference “(from 129T_CRS)” indicates that any following    constant is provided by its register (belonging to the Control    Register Set for 129 Tone), wherein this register is loaded by PCU    in order to control operations of the Real Time Processor for 129    Tone (129T_RTP);-   the coefficient k equals to 2×129=258 for the 129 Tone;-   the resonating IIR filtering begins after the CFR3(S0) is produced    after collecting N+1=512 samples in CFR2(S0:S511);-   the Resonator Filter Register is reset by the signal    RESET_RFR(S0:S3) before any new tone IIR filtering begins,-   and furthermore such IIR filtering of an entire sequence of N+1=512    samples is completed before using resulting RFR outputs for any    further signal processing.

Similar resonating IIR filter designated for the 129.5Sub-Tone (129.55T)is shown in FIG. 4A, wherein:

-   the reference “(from 129.5ST_CRS)” indicates that any following    constant is provided by its register (belonging to the Control    Register Set for 129.5ST), wherein this register is loaded by PCU in    order to control operations of Real Time Processor for 129.5Sub-Tone    (129.5ST_RTP);-   the coefficient k equals to 2×129.5=259 for the 129.5Sub-Tone;-   the resonating IIR filtering begins after the CFR3(S0) is produced    after collecting N+1=512 samples in CFR2(S0:S511);-   the Resonator Filter Register is reset by the signal    RESET_RFR(S0:S3) before any new tone IIR filtering begins,-   and furthermore such IIR filtering of an entire sequence of N+1=512    samples is completed before using resulting RFR outputs for any    further signal processing.

This disclosure comprises implementation of integrating and/or averagingtime domain filter with a circularly driven register (i.e consecutiveprocessed samples are clocked in circularly into the register) supplyingsuch filter's integrating/summating circuits with a proper set ofintegrated/summated samples.

Such time domain filter achieves integration/summation over aconsecutive set containing a required number of samples, by circularreplacing of the first sample of a previous set, stored in the circularregister, with a new sample following the last sample of the previousset. Resulting consecutive set of samples on the circular registeroutputs is supplied to the filter integrating/summating circuitproducing filter output.

Such time domain filter is exemplified in FIG. 5 where it is used forintegration of 129T Half-Cycles and for detecting phases of suchHalf-Cycles (HC) ends, wherein an end of the present HC occurs at thebeginning of the next opposite HC.

Since the input to such HC integrating filter has already been filteredby the previous stages FSF, such input must have sinusoidal shape.Therefore resulting integral of amplitudes of 129T HC representsfiltered indicator of original amplitude of the 129T sinusoid. Suchintegral is used for the recovery of the original tone amplitude as itexplained later on.

Since such time domain filter and all the previous filters belong to theSCP operating in phase with the Tones Frame (DMT Frame), such detectedHC phase is used for recovering phase of the originally transmitted HCof 129T.

The outputs of the 129T Resonator Filter Register (129RFR(S0:S3)) areclocked in circularly into the Stage5/129 Half Cycle Register(129HCR(S0:S15)) which comprises 16 samples covering an approximated HCinterval.

The outputs of 129HCR are connected to the summating circuits producingan integral of the last 16 samples long sequence (named Next Integer(NI)).

While Next Integral (NI) of amplitudes of HC long interval is calculatedand fed to the Integral Register (IR(0:K)); it is also compared with thePrevious Integral (PI) kept in Previous Integral Buffer (PIB(0:K)), inorder to verify if Half-Cycle end is reached.

Such HC end occurs when NI<PI/NI>PI is detected followingpositive/negative HC accordingly.

When the end of positive/negative HC is detected, the integral ofamplitudes over positive/negative HC is loaded into 129 Positive Amp1.Reg. ((129PAR((0:K))/129 Negative Amp1. Reg. (129NAR(0:K)) by signal129Ld_PA/FE/129Ld_NA/RE accordingly.

Signals 129Ld_PA/FE/129Ld_NA/RE are generated by DecCLK/IncCLK strobes,only if IncCTR>5/DecCTR>5 condition is met. The purpose of suchpreconditioning is prevention of oscillations (such as caused bycomputational instability at small signal amplitudes), by providinghisteresis introduced by Inc.Counter(0:2)/Dec.Counter(0:2) forpositive/negative HC accordingly.

Said IncCTR>5/DecCTR>5 conditions are possible only when the multi-toneprocessing inhibition signal MTP_Inh is de-activated after initial 640sampling periods of every new DMT frame (see FIG. 5 and FIG. 6).

The 642 Decoder (shown in the block 8 in FIG. 6) decodes such 640samples delay introduced by waiting until 640+2 sampling intervals arecounted by Frame Samples Counter (FSC), wherein the additional 2intervals account for the 2 sampling intervals occurring between theMTP_Inh generation in the SCP stage 4 and its actual application in theSCP stage 6.

In addition to the prevention of IncCTR>5/DecCTR>5 conditions, MTP_Inhsignal inhibits any generation of 129Ld_PHC/129Ld_NHC (see FIG. 6), andthus MTP_Inh makes sure that no time domain processing takes placebefore valid signals are supplied by the frequency domain filters.

Circuit shown in FIG. 5A providing “Half Cycles Integration & AmplitudesRegistration for 129.5 Tone”, performs the same operations as thecircuit shown in FIG. 5 described above.

Since SCP operations are driven by clocks and sub-clocks having knownphase and frequency relation to DMT Frame, results produced by SCPstages have known phase relations to DMT Frame as well. Therefore suchdetection of an end of positive/negative HC can be used to detect phaseof Tone cycle producing such HC.

As such detection of positive/negative HC end signals detection offalling/rising edge of 129 Tone sinusoid as well, signal129Ld_PA/FE/129Ld_NA/RE is used in FIG. 6 for capturing phase of suchfalling/rising edge by capturing 129 Tone Phase in 129 Falling Edge Reg.(129FER(0:5))/129 Rising Edge Reg. (129RER(0:5) accordingly.

This FIG. 6 shows Phase Capturing and Tone Processing Initialization forthe 129T/128.5ST/129.5ST, wherein:

-   the reference “(from 129T_CRS)” indicates that any following    constant is provided by its register (belonging to the Control    Register Set for 129 Tone), wherein this register is loaded by PCU    in order to control operations of Real Time Processor for 129 Tone    (129T_RTP);-   since 129RisingEdgeReg./129FallingEdgeReg. captures the end of a    negative/positive half-cycle, it represents phase of the    rising/falling edge accordingly of a sinusoid represented by such    negative/positive half-cycle.

Such 129 Tone Phase is produced by subtracting 129 Last Cycle Phase Reg.(such 129LCPR(0:13) specifies nr. of sampling intervals corresponding tothe beginning of the presently expected cycle of 129 Tone) from FrameSamples Counter (such FSC(0:12) specifies nr. of sampling intervalswhich past from the beginning of the present DMT Frame).

Consequently such capture of the 129 Tone Phase defines phase ofpresently detected cycle of 129 Tone measured in number of samplingintervals which occurred between the beginning of the expected cycle(having 0 phase) and the detected 129T cycle.

Content of 129LCPR is derived by comparing if FSC-LCPR=129 Cycle (129Cycle represents number of sampling intervals expected duringconsecutive 129Tone cycle), and by loading FSC into LCPR whenever suchequality condition is fulfilled.

In order to avoid accumulation of digitization errors during suchmultiple comparisons (involving fractional numbers expressing expectedlengths of 129Tone cycles);

-   a method using fractional bit staffing (described also in public    domain) can be applied by adding consecutive bits from Fractional    Bits Register (FBR(0:128)) to 129CycleBase(0:4).

These additions provide consecutive values of 129Cycle(0:5) keepingtotal digitization error below single sampling interval.

SCP combines in-phase processing in frequency domain with in-phaseprocessing in time domain.

Therefore SCP detects time/phase dependence between noise sub-bands andDMT Tones.

Consequently SCP enables estimating and compensating impact of neighbornoise sub-bands and neighbor tones on specific cycles of particulartones.

Such estimates and compensation use data from training session and fromadaptive wave-form sampling and screening for identifying noise patternsand for programming compensation and inverse transformation coefficientsby PCU.

Such detection of phase relations is facilitated by capturing a fallingedge of positive HC of 129T/128.5ST/129.5ST in129FER(0:5)/128.5FER(0:5)/129.5FER(0:5) by signal129Ld_PA/FE/128.5Ld_PA/FE/129.5Ld_PA/FE.

Similarly a rising edge of negative HC of 129T/128.5ST/129.5ST iscaptured in 129RER(0:5)/128.5RER(0:5)/129.5RER(0:5) by signal129Ld_NA/RE/128.5Ld_NA/RE/129.5Ld_NA/RE.

In order avoid using incomplete HC detected at a beginning of DMT Frame,second appearance of signal LD_PA/FE/LD_NA/RE is required in order toproduce signal 129Ld_PHC/129Ld_NHC enabling further processing of129Tone shown in FIG. 7.

This FIG. 7 shows Retiming & Averaging of Positive and Negative HC for129T/128.5ST/129.5ST, wherein:

-   the content of 129FER/128.5FER/129.5FER is processed by the    “Modulo-Cycle Adder of Half-Cycle” converting a phase of falling    edge into a corresponding phase of rising edge,-   wherein this phase of rising edge represents phase of sinusoid    defined by a positive half-cycle ending at the time instant captured    in 129FER/128.5FER/129.5FER.

The 7/Clk shown in FIG. 7 generates single PHC/Clk/CYC/Clk impulse if itdetects Ld_PHC/Ld_NHC timed by 6/Clk. Such PHC/Clk re-times129RER(0:5)/128.5RER(0:5)/129.5RER(0:5) by re-loading them into129REB(0:5)/128.5REB(0:5)/129.5REB(0:5), which are:

-   averaged with 129FER(0:5)/128.5FER(0:5)/129.5FER(0:5) converted into    cycle edge by Modulo-Cycle Addition of Half-Cycle);-   and re-timed with CYC/Clk loading them into    129AER(0:5)/128.5AER(0:5)/129.5AER(0:5).

The positive amplitude registers 129PAR(0:K)/128.5PAR(0:K)/129.5PAR(0:K)are averaged with 129NAR(0:K)/128.5NAR(0:K)/129.5NAR(0:K) accordinglyand loaded into the averaged amplitude registers129AAR(0:K+1)/128.5AAR(0:K+1)/129.5PAR(0:K+1).

SCP comprises using every positive or negative HC as separate data usedfor recovering a tone symbol. Such ability of using singular Half-Cyclesfor data recovery provides a huge data redundancy which facilitates useof statistical methods much more reliable than conventionally used DFTaveraging over DMT Frame.

Nevertheless, in order to illustrate implementation having lower powerdissipation; SCP exemplified by this embodiment has 7th stage (see FIG.7) combining amplitudes and phases of positive and negative HC intoaverages per cycle (which still provide significant redundancy).

The NFIT comprises an inversion of frequency related distortions in atransmission channel (such as DMT link), by applying differentnormalizing coefficients to different Carrier Frequencies (such as DMTTones) wherein such normalizing coefficients are adjusted to equalizeamplitude and phase distortions of the transmitted Carrier Freq.including distortions introduced by a signal processing applied; suchinverse normalization of amplitudes and phases comprises:

-   identification of the frequency related distortions occurring on the    Carrier Frequencies (or DMT Tones) by using training sessions or    adaptive wave-form sampling/screening controlled by PCU;-   calculating normalizing coefficients, for such Carrier Frequencies    or DMT Tones, by PCU;-   using such normalizing coefficients, supplied by PCU, by real-time    processing unit for equalizing such frequency related distortions in    the processed Carrier Freq. or DMT Tones.

Such amplitude and phase normalization for 129T/128.5ST/129.5ST is shownin FIG. 8, wherein it includes normalization of noise sensing Sub-Tones(128.5ST/129.5ST) neighbor to the data carrying 129T.

129 Tone phase defined by 129 Tone Averaged Edge Register (129AER(0:5)),is normalized by multiplying by the 129T Phase Normalizing coefficient(129PhaNor) and by adding the 129T Phase Adjusting coefficient(129PhaAdj).

Since sinusoidal noise contribution from such neighbor sub-tones isdependent on phase differences between the tone and the sub-tones, suchphase differences are normalized by multiplying them by the PhaseNormalizing coefficient.

129T Averaged Amplitude Register (129AAR(0:K+1)) and its 128.5ST/129.5STcounterparts ((128.5AAR(0:K+1)/(129.5AAR(0:K+1)) are normalized bymultiplying them by the 129T Amplitude Normalizing coefficient(129AmpNor).

All such normalizing coefficients are taken from the 129 Tone ControlRegister Set (129T_CRS) which is pre-loaded by PCU implementing adaptivedistortion reversing techniques.

While SCP comprises performing signal processing operations which aresynchronized by the processed incoming signal, such approach comprisestwo different synchronization methods specified below and exemplified bythe embodiments shown herein.

When SCP stages (such as previous 7 stages) perform processing ofbelonging to frequency domain DMT Tones (or Multi-Band carriers); theyare synchronized by DMT Frame (or channel frame), as such stages aredriven by the clocks or sub-clocks synchronous to the sampling clockwhich is phase locked to DMT Frame (or channel frame).

When SCP stages (such as this 8^(th) stage and next stages) performprocessing of already detected tone (or band) cycles belonging to timedomain; they are synchronized by such cycles detection events instead,as such stages are driven by clocks generated when information aboutcycle detection is passed from a higher level stage to the next level.

Such second synchronization method does not do (discontinues) anyfurther processing if a new cycle of the tone (or band) is not detected.

SCP comprises both synchronization methods defined above.

The cycle detection signal CYC/Clk enables using leading edge of 8/Clk/8(having frequency 8 times lower than the sampling clock) for the onetime activation of AS1/Clk signal which drives all the registers of theSCP 8th stage presented in FIG. 8.

Such AS1/Clk signal remains active (for about 1 sampling period) untilthe leading edge of the next 9/Clk signal activates the AS1_RST signal(see FIG. 9A).

Such AS1_RST signal enables using leading edge of the next 8/Clk/8 forthe one time activation (for about 1 sampling period) of the signalwhich initiates reading of amplitude and noise compensation coefficientsfrom Memory of Noise Compensation Coefficients (MNCC).

Such timing enables Address Decoders for Memory of Noise CompensationCoeff. (AD_MNCC) to have processing time extended to 8 samplingintervals in order to use normalized amplitudes and phases provided bythe previous 8th stage for decoding Address(0:8)/NS_MNCC beforeAS2/Read_MNCC is activated.

The NFIT comprises an efficient non-linear reversing of transmissionchannel distortions and non-linear noise compensation in over-sampledsignals, by implementing real time processing units (RTPs) usingsimplified algorithms applying variable coefficients, wherein such RTPsare controlled by the back-ground processing PCU which implementsadaptive non-linear algorithms by analyzing received line signal andintermediate RTPs processing results and by defining and down-loadingsuch coefficients to content addressed memories accessed by RTPs such a129 Tone Control Registers Set (129T_CRS) or Memory of NoiseCompensation Coefficients (MMCC).

Such NFIT noise compensation method comprises RTP operations listedbelow:

-   frequency domain and/or time domain processing of data carrying    signal and/or neighbor tones or frequency bands in order to derive    estimates of parameters influencing distortion or noise components    in the signal, wherein such parameters may include amplitudes and/or    phase of data carrying tone or freq. band and/or surrounding noise    or interference from neighbor tones or bands;-   converting such parameters into an effective address of said content    addressed memory in order to access coefficients providing most    accurate compensation of said channel distortion or noise;-   applying such coefficients to a sequence of predefined arithmetic    and/or logical operations performed on the received signal in order    to reverse transmission channel distortions and/or to improve signal    to noise ratio.

Such noise compensation method is illustrated in FIG. 9A-FIG. 9B andFIG. 10 showing stage 9^(th) and 10^(th) of the SCP embodiment.

It is shown in FIG. 9A that:

-   said noise affecting parameters supplied by 129T/129.5ST Normalized    Amplitude Registers (abbreviated to 129NAR(0:P)/129.5NAR(0:P)) and    129.5ST Averaged Phase Difference Register (abbreviated to    129.5APDR(0:L)-   are used together with the 129 Tone Amplitude Thresholds/Next    Sub-tone Amplitude Thresholds and Next-Sub-tone Phase Difference    Thresholds,-   in order to decode address to the Next Sub-tone MNCC    (Address(0:8)/NS_MNCC).

It is detailed in FIG. 9A that:

-   said 129 Tone Amplitude Thresholds facilitating use of different    coefficients programmed adaptively by PCU, are applied as T/AT1,    T/AT2, . . . T/AT6, T/AT7;-   said Next Sub-tone Amplitude Thresholds facilitating use of    different coefficients depending on 129.5ST Amplitude, are applied    as NS/AT1, NS/AT2, . . . NS/AT6, NS/AT7;-   said Next Sub-tone Phase Difference Thresholds facilitating use of    different coefficients depending on 129.5 ST Phase Difference, are    applied as NS/PDT1, NS/PDT2, . . . NS/PDT6, NS/PDT7;-   said Address(0:8)/NS_MNCC selects reading & loading of coefficients,    compensating expected noise contribution from the 129.5ST, to their    registers 129.5Amp1. Addition Reg/129.5 Amp1. Multiplication    Reg./129.5 Phase Addition Reg./129.5 Phase Multiplication Register.

Very similar circuits and methods (shown in FIG. 9B) addressing thePrevious Sub-tone MNCC (Address(0:8)/PS_MNCC) are applied in order toselect & load coefficients, compensating expected noise contributionfrom the 128.5ST, to their registers 128.5Amp1. Addition Reg/128.5 Amp1.Multiplication Reg./128.5 Phase Addition Reg./128.5 Phase MultiplicationRegister.

These registers, loaded from NS_MNCC and PS_MNCC, supply coefficientsproducing estimates of noise compensating components which are added to129T amplitude and to 129T phase (as shown in FIG. 10), in order toprovide compensated amplitude in 129 Compensated Amplitude Register(129CAR(0:P,ERR)) and compensated phase in 129 Compensated PhaseRegister (129CPR(0:L,ERR)).

Such noise compensating coefficients are derived by PCU based onevaluations of noise patterns occurring in tones frequency region andtheir contributions to signal noise acquired during training session andsupported by adaptive wave-form sampling and screening utilizing widecoverage of almost entire spectrum by Tones and Sub-tones detected withsaid FSFs.

The NFIT comprises:

-   detecting noise patterns occurring in frequency domain by using    frequency domain processing such as Frequency Sampling Filters for    noise sensing in a wide continous frequency spectrum incorporating    data carrying tones or frequency bands;-   detecting noise patterns occurring in time domain by using time    domain processing for noise sensing over time intervals including    tone (or band) reception intervals;-   using back-ground PCU for analyzing such detected noise patterns and    for creating deterministic and random models of occurring noise    patterns;-   using such models of noise patterns for deriving noise compensation    coefficients used by the Real Time Processors for improving signal    to noise ratios in received data carrying signal;-   taking advantage of the recovered symbols redundancy (assured by the    RTPs time domain processing ability of recovering data symbol from    every tone cycle) by applying such noise models for estimating    probability of symbols recovered and/or for dismissing symbols    accompanied by high noise levels close in time;-   using such probability estimates and/or dismissals of unreliable    symbols for applying statistical methods which are more reliable    than conventional DFT averaging of tone signal received.

Such ability of said symbol dismissal, if detected in a vicinity of highnoise, is illustrated in FIG. 10, wherein:

-   the comparison is made if the sum of 129T Amplitude Noise Components    (128.5/129.5Amp.NoiseComp.) exceeds 129 Maximum Amp1. Noise    (abbreviated to 129Max.AmpNoise) pre-loaded to 129T-CTRS by PCU as a    total limit on both acceptable compensations from 128.5ST and    129.5ST taken together.-   if the comparison 128.5ANC+129.5ANC>129MAN, the ERROR bit marking    such symbol for dismissal is written to the 129CAR(0:P,E).

Similarly for the 129T Phase Noise Components(128.5/129.5Pha.NoiseComp.):

-   if the comparison 128.5PNC+129.5PNC>129MPN, the ERROR bit marking    such symbol for dismissal is written to the 129CAP(0:L,E).

The NFIT comprises a method for recovery of data symbol transmitted by asingular half-cycle/cycle of said DMT or Multiband tone, wherein:

-   an amplitude measure of said singular half-cycle/cycle, such as    integral of amplitude over the half-cycle/cycle time period, and a    phase measure of the half-cycle/cycle, are applied to a symbol    decoder transforming such combination of amplitude and phase    measures into a number representing said recovered data symbol.

Such symbol recovery method further comprises:

-   comparing said amplitude measure to predefined amplitude thresholds,    in order to decode an amplitude related factor in a recovered symbol    definition;-   comparing said phase measure to predefined phase thresholds, in    order to decode a phase related factor in recovered symbol    definition;-   wherein such amplitude and phase comparators produce their parts of    a binary address to a content addressed memory storing a counter of    half-cycles/cycles detecting said symbol occurrences during said DMT    or Multi-band signal frame;-   wherein such symbols counters memory (SCM) can accommodate different    symbols, detected during said DMT or Multi-band frame, varying    during the same frame due to said channel distortions and changing    in time noise distribution;-   sorting symbols, carried by singular half-cycles/cycles, detected    during said DMT or Multi-band frame, in accordance to their    detections numbers and/or distributions;-   application of statistical methods for selecting data symbol    recovered, from said DMT or Multiband tone, such as selection of a    symbol having higher detections number in a range outlined with    statistical distribution models.

Implementation of such data symbol recovery, is exemplified in FIG. 11.

-   A DMT control registers set (DMT_CRS) programmed adaptively by PCU,    supplies said amplitude thresholds (AT1, AT2, AT3, AT4) and said    phase thresholds (PT1, PT2, PT3, PT4) to address decoder for symbols    counts memory (AD_SCM); wherein:-   said AT1, AT2, AT3, AT4 (programmed adaptively by PCU) represent    Amplitude Thresholds digitizing recovered amplitude;-   said PT1, PT2, PT3, PT4 (programmed adaptively by PCU) represent    Phase Thresholds digitizing recovered phase.

AD_SCM digitizes compensated amplitude/phase provided byCAR(0:P)/CPR(0:L) by comparing them with said amplitude and phasethresholds, in order to produce address ADR(0:3) equal to binary code ofsymbol detected.

Such ADR(0:3) is applied (as ADR/SCM) to the symbols counts memory (SCM)when the read-write signal (Rd-Wr/SCM) initializes read-write cycle in129T symbol counts memory (129SCM).

In response to such Rd-Wr/SCM signal said 129SCM provides a content of asymbol counter (129Symb.Count(0:8)) identified by said ADR/SCM.

129Symb.Count is increased by 1 and is written back to the same symbollocation in SCM (as updated counter CNT-UPD(0:8)/SCM), if 129SymAcc=1(i.e. if both Error Bits CAR(E) and CPR(E) are inactive).

However; 129Symb.Count remains unchanged when it is written back to thesame SCM location, if 129SymAcc=0 (i.e. if CAR(E) or CPR(E) is active).

Maximum Count of detections of the same symbol discovered in present129T, is stored in 129Max.CounterReg. (129MCR(0:8)) which is read by PCUat the end of DMT frame.

Any consecutive updated counter CNT-UPD/SCM (abbreviated as 129SC+1) iscompared with such 129Max.CounterReg. (abbreviated as 129MCR).

If (129SC>129MCR)=1; the newly updated counter is loaded to said129Max.CounterReg., and the address of the newly updated counter (equalto the binary code of the symbol detected) is loaded to129Max.Cont.Addr.Reg. (129MCAR(0:3) which is read by PCU at the end ofDMT frame.

Otherwise if (129SC>129MCR)=0; both 129Max.CounterReg. and129Max.Cont.Addr.Reg. remain unchanged.

In order to simplify further PCU operations; there is a 129T detectedsymbols map register (129DSMR(1:16)) which has 16 consecutive bitsdesignated for marking occurrence of the 16 consecutive symbols duringDMT frame, wherein particular marking bit is set to 1 if correspondingsymbol occurs one or more times. Such 129DSMR(1:16) is read by PCU atthe end of DMT frame.

2. Embodiments of IST

The DRPS OFDM described in configuration 6 and shown in FIG. 13D, isdesigned to compare half-cycles/cycles of tones/sub-bands received fromthe resonating filters (see FIG. 4) with reference frames havingexpected sinusoidal contours in order to detect which of such frames isthe closest one.

Such comparison can be accomplished using comparator shown in FIG. 13C(explained further below) as producing deviation integrals forcontinuing sinusoidal outputs produced by the resonating filters.

Averaged values of such deviation integrals can be used to select one ofreference frames applied to this tone/sub-band as being the closest oneand thus being useful for recovering amplitude related component of thedata symbol.

Occurrence of minimum values may indicate phase of the tone/sub-band andthus can be useful for recovering phase related component of the symbol.

Samples of an interval of said received or preprocessed signal may becompared with elements of a reference frame as it is shown in FIG. 13Cand explained below.

For a signal interval ending with a sample Sk of a signal, earliersamples S_(k+1) of said sample S_(k), may be defined by using 1 rangingfrom 0 to 15 if it is assumed that this interval is 16 samples long.

For such interval a deviation of its sample S_(k+1) from itscorresponding element R₁ of a reference frame may be calculated asModulus of (S_(k+1)-R₁).

Consequently for every such interval, its deviation integral DI_(k) maybe calculated as equal to:

${DI}_{k} = {\sum\limits_{l = 0}^{15}{{S_{k + l} - R_{l}}}}$

Estimates of minimums of such deviation integrals may be used to verifyif:

-   the interval comprises a data carrying contour (such as an edge of    PAM or a half-cycle or cycle of tone or sub-band of OFDM);-   the frame is close enough to estimate a range of amplitude and phase    represented by the contour in order to identify received or    preprocessed signal subspace which this contour belongs to,-   wherein this particular subspace is predefined as carrying specific    data by the inverse transformation algorithm.

As it has been indicated in the NFIT embodiment, the direct datarecovery may be achieved by using such contours subspace identifiers foraddressing Content Addressed Memory, pre-loaded with data implementingsaid inverse transformation algorithm.

Since this embodiment deviation integrals result from adding positivedeviations between single samples and their mask counterparts, minimumvalues of such integrals indicate edge occurrences.

Such frame (having amplitude and phase attributes assigned to it) canrecover specific data symbol encoded originally into a particularhalf-cycle/cycle by addressing a Content Addressed Memory.

The DRPS PSP OFDM described in configuration 9 and shown in FIG. 13B, isdesigned to use:

-   amplitude integrals, produced by NFIT circuits for amplitude    integration and registration (see FIG. 5) and phases of    half-cycles/cycles, supplied by NFIT circuits for phase capturing    (see FIG. 6),-   for selecting reference having amplitudes and phases close enough to    limit number of comparisons made simultaneously for high order OFDM    codes applied.

Such selection of close frames can be followed by comparisonsinterpretations and symbol recovery similar to those described above.

The DRPS PSP OFDM configuration shown in FIG. 13E, is optimized(compared to the configuration 12 and another presented in “Embodimentsof NFIT”) by direct addressing of Content Addressed Memory withamplitude integrals and phase captures in order to recover data symbols,

-   instead of comparing these integrals and phase captures with their    references (as it's shown in configuration 12) or thresholds (as    it's shown in “Embodiments of NFIT”) before applying the inverse    transformation of sub-ranges identified by these comparisons.

3. Embodiments of DDR and ADD

The embodiment of DDR PSBC described in the section “2. Summary of DDR”is shown in FIG. 14 and explained below.

The programmable control unit (PCU) reads received signal samplessupplied by a waveform screening and capturing circuit (WFSC).

The PCU performs background processing in order to derive and keepupdating a relation between said data transmitted originally and saidsubranges of amplitudes/phases of cycles or half-cycles of sub-bandsignals.

Such derivation is based on theoretical models of transmission channelsand/or training sessions and an analysis of said received signal samplessupplied by the WFSC.

The WFSC is described in the sec. “2. Summary of NFIT” on page 47 andits configuration with PCU is described in the same section on page 30.

The PCU specifies such relation by:

-   producing said references defining said amplitudes/phases subranges    and an assignment of said specific transmitted symbols as    corresponding to said specific amplitudes/phases subranges;-   supplying said assignment of specific transmitted symbols as    corresponding to specific subranges of amplitudes/phases detected by    the Received Signal Processor (RSP) performing real time processing.

The PCU may also control said real time processing performed by the RSPand modify coefficient used in RSP operations in order to improve theirefficiency.

Such RSP implementations are exemplified herein with the circuitsdescribed in the sec. “1. Embodiments of NFIT” on pages 51 and 53-59 andshown in FIG. 3-FIG. 6.

The Adaptive Data Decoder (ADD) is shown in FIG. 15.

The References Register is loaded from PCU with references definingsubranges of parameters (amplitudes/phase of cycles or half-cycles).

Such signal parameters are compared to these references by theComparator in order to detect which references is this parameter theclosest to.

The Comparator uses such closest references to define a binary addressof a cell of the Content Addressed Memory (CAM) which contains datasymbol loaded to the CAM by the PCU signal assigning data symbols toparameters sub-ranges.

The ADD recovers data symbol corresponding to the signal parameterssupplied to it from the RSP, by simply reading this cell from the CAM.

Such DDR configuration can accommodate instantly fast changingcharacteristics of the received signal and/or transmission channel,

-   since the RSP can produce signal parameters characterizing channel    interferences and/or history of a particular signal, in addition to    parameters characterizing said particular signal received presently.

Such additional parameters can be compared with their references derivedand supplied by PCU, in order to modify said CAM address to one whichselects a cell containing appropriate data symbol.

Such additional parameters can be derived to characterize distortionsintroduced by signals having adjacent frequencies contributed by theenvironment of a particular sub-band including external noise or othercomponents of received signal.

The frequency sampling configurations disclosed in the RSP's RTP (seepages 51, 53-59) can perform on chip spectrum analysis exemplifiedtherein by detecting and characterizing parameters of intermediatefrequencies occurring between data carrying sub-bands.

This application includes using such parameters of intermediatefrequencies for reversing effects of distortions caused by a noisesampled at these intermediate frequencies,

-   by applying them to and using by the same ADD-PCU configuration as    the parameters related to specific sub-bands only described above.

The embodiment of DDR SBS described in the section “2. Summary of DDR”is shown in FIG. 16 and explained below.

This embodiment of DDR SBS preserves the features of DDR PSBC describedabove with the exception of two structural differences described below.

It uses sub-band signals as referencing signals for identifyingreferencing subspaces instead of using signal parameters for identifyingparameters subranges, in order to address CAM and recover data symbolsfrom it.

Therefore it needs deviation integrals calculators and analyzer andreference frames selector/identifier shown in the ADD presented in FIG.17.

Such deviation integrals calculators needed for decoding data from OFDMsub-bands have been already described and shown in FIG. 13C of IST.

Such utilization of deviation integrals instead of straight parameterscan allow more reliable data recovery which can be more desirable inBase Stations communicating with mobile devices transmitting lesspowerful signals.

The embodiment of DDR PSB described in the section “2. Summary of DDR”is shown in FIG. 18.

It keeps general features of the PSBC but it does not requireoversampling or recovery of individual cycles or half-cycles of OFDMsub-bands.

Therefore its applications include FFT based OFDM receivers usedcommonly as it is shown in FIG. 18.

The embodiments of the ADD systems and circuits presented in FIG. 13C,FIG. 16 and FIG. 17 comprise solutions shown in the FIG. 19. FIG. 20 andFIG. 21;

-   wherein integrals of tones amplitude gradients instead of the    integrals of tones amplitudes are calculated and used as tones    parameters utilized for identifying the referencing subspaces and    recovering data which these subspaces correspond to.

This identification of referencing subspaces and the data recovery areconducted by:

-   using these gradient integrals for selecting reference frames    (defining the referencing sub-spaces) which are expected to be the    closest to the received tones;-   calculating integrals of deviations between the received tones and    the selected reference frames;-   detecting minimums of the deviation integrals in order to identify    the closest reference frame and the referencing sub-space defined by    the closest reference frame;-   wherein the selection of the reference frames may be accomplished by    using the gradient intervals for addressing a Content Addressed    Memory (preloaded by the PCU based of the signal analysis) and    reading the closest frames;-   wherein the closest frame may be used for addressing another Content    Addressed Memory (preloaded by the PCU as well) in order to read    data corresponding to the referencing subspace defined by the    closest frame.

It is shown in FIG. 19 that the calculation and registration of thegradients integrals may be implemented with the circuit very similar tothat shown in FIG. 5 and described in &3/page 57-&1/page 58.

The only differences are described below.

An integral of gradients around a specific middle signal sample iscalculated by:

-   subtracting amplitudes of previous samples from amplitudes of    corresponding following samples shifted forward by the same number    of sampling periods as the previous samples are trailing the    specific middle sample;-   adding all the subtraction results derived over a half-cycle    surrounding the specific middle sample.

In order to implement such algorithm with signals available in FIG. 19the following equation needs to be implemented:

${{Next}\mspace{14mu}{{Integral}({NI})}} = {{\sum\limits_{k = 0}^{k = 7}{5/{{QCR}({Sk})}}} - {\sum\limits_{k = 0}^{k = 7}{6/{{QCR}({Sk})}}}}$

Therefore the half-cycle sub-tone interval is split into twoquarter-cycle intervals which are pre-loaded into two different stages5^(th) and 6^(th) instead of being accommodated in the 5^(th). stage aspreviously.

Consequently the registrations of maximum gradient integrals in the129Positive Grad.Register and minimum gradient integrals in the129Negative Grad. Register are delayed by one stage and are done in the7^(th) stage instead of the 6^(th) .stage.

FIG. 20 shows selecting reference frames by reading them from theContent Address Memory of Reference Frames (CAM RF), wherein:

-   the maximum and minimum gradients represented by the outputs of the    gradient registers named 129PGR(0:K) and 129NGR(0:K) (selected by    signals 129Ld_PG/FE and 129Ld_NG/RE accordingly) provide suitable    addresses to the CAM RF;-   the selected reference frame (Read Ref. Frame RRF(0:15)) is read and    loaded to selected frame register (Selected Ref. Frame RF(0:15))    synchronously to the tone half-cycle currently inserted into the    shift register (ShiftReg SR(0:15)) from the half-cycle register    (7/129HalfCycleReg. 7/129HCR(0:15)).

Such synchronous operations essential for providing correspondingarguments to the Deviation Integral Arithmometer are controlled bysynchronization circuit generating read request signal when maximum andminimum gradients are detected,

-   wherein the 129Ld_PG/FE and 129LD_NG/RE are activating proper bit in    the register 7/129RRR(0:15) prompting the read request signal Read    Request and causing the data ready signal Data Ready to appearing by    one half-cycle before the half-cycles having these maximum and    minimum gradients.

Such by half-cycle forward displacement allows reference frameapplication to the half-cycles preceding detections of maximums andminimums occurring at the end of half cycles.

The minimum of deviation integrals (see 129MinimumDeviationRegister129MDR(0:D)) is detected as shown in FIG. 21 with the circuit verysimilar to that explained in the &3/page 57-&1/page. 58.

These minimums of deviation integrals are utilized for recovering datasymbols from tone half-cycles by using the configuration of the ContentAddress Memory of Data Symbols (CAM DS) and supporting circuits in theconfiguration very similar to that explained above as used for readingselected reference frames from the CAM RF.

It shall be noted that using such integrals of half-cycles of gradientsinstead of amplitude has the advantage of eliminating problems with anyfloating of DC level in OFDM tones.

The above gradient based solution exemplifies more complex method wheresubranges of gradients (used as tones parameters) are used to selectclosest reference frames detecting tones subspaces which are inverselytransformed to detect original data.

It shall be noted the ADD solutions contributed herein also includesimpler solutions wherein:

-   subranges of gradients (used as tones parameters) are subjected    directly to said inverse transformation recovering original data    without even using said reference frames;-   or saids reference frames are applied directly to the tones and use    of parameters for these frames selection is avoided.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein, but is to beaccorded the full scope consistent with the claims, wherein reference toan element in the singular is not intended to mean “one and only one”unless specifically so stated, but rather “one or more”. All structuraland functional equivalents to the elements of the various embodimentsdescribed throughout this disclosure that are known or later come to beknown to those of ordinary skill in the art are expressly incorporatedherein by reference and are intended to be encompassed by the claims.Moreover, nothing disclosed herein is intended to be dedicated to thepublic regardless of whether such disclosure is explicitly recited inthe claims.

What is claimed is:
 1. A method for adaptive data recovery (ADR) from a received orthogonal frequency division multiplexing (OFDM) signal by an adaptive decoding of data symbols from intervals of tone signals of the received OFDM signal wherein the intervals of the tone signals correspond to cycles or half-cycles of the tone signals, wherein the received OFDM signal is produced by a coding circuit implementing a coding of the data symbols into a transmitted OFDM signal and a transmission link altering the transmitted OFDM signal into the received OFDM signal in accordance to a current transfer function; wherein the method for ADR comprises the steps of: producing, using a received signal processor, the intervals of the tone signals from the received OFDM signal; deriving the current transfer function by utilizing parts of the received OFDM signal corresponding to known parts of the transmitted OFDM signal; wherein the current transfer function includes dynamic distortions introduced to the received OFDM signal by time variant changes of characteristics of the transmission link; defining distinctive sets of the intervals of the tone signals; wherein each of the distinctive sets comprises the intervals of the tone signals expected to represent one of the data symbols; preloading the data symbols to a content addressed memory; identifying ones of the distinctive sets comprising the produced intervals by processing the produced intervals; decoding adaptively the data symbols by reversing both the coding of the data symbols and the current transfer function, wherein the reversing of both the coding of the data symbols and the current transfer function is achieved by reading the data symbols from the content addressed memory addressed with identifiers of the identified distinctive sets.
 2. A method as claimed in claim 1, wherein the ADR method further comprises: continuous updating of the current transfer function based on an analysis of the parts of the received OFDM signal corresponding to the known parts of the transmitted OFDM signal.
 3. A method as claimed in claim 1, wherein the ADR method further comprises: continuous updating of the current transfer function based on an analysis of the received OFDM signal, in order to accommodate a gradual fading of the received OFDM signal.
 4. A method as claimed in claim 1, wherein the defining distinctive sets of the intervals of the tone signals comprises: using a known pattern of the coding of the data symbols and the current transfer function of the transmission link.
 5. A method for adaptive data recovery (ADR) from a received orthogonal frequency division multiplexing (OFDM) signal by an adaptive decoding of data symbols from parameters of tone signals of the received OFDM signal, wherein the received OFDM signal is produced by a coding circuit implementing a coding of the data symbols into a transmitted OFDM signal and a transmission link altering the transmitted OFDM signal into the received OFDM signal in accordance to a current transfer function; wherein the method for ADR comprises the steps of: producing, using a real time processor, the parameters of the tone signals from the received OFDM signal wherein the produced parameters correspond to amplitudes or phases of the tone signals, wherein operations of the real time processor are controlled by a background processor; deriving the current transfer function by utilizing parts of the received OFDM signal corresponding to known parts of the transmitted OFDM signal; wherein the current transfer function includes dynamic distortions introduced to the received OFDM signal by time variant changes of characteristics of the transmission link; defining distinctive sets of the parameters of the tone signals; wherein each of the distinctive sets comprises the parameters of the tone signals expected to represent one of the data symbols; preloading the data symbols to a content addressed memory; identifying ones of the distinctive sets comprising the produced parameters by processing the produced parameters; decoding adaptively the data symbols by reversing both the coding of the data symbols and the current transfer function, wherein the reversing of both the coding of the data symbols and the current transform function is achieved by reading the data symbols from the content addressed memory addressed with identifiers of the identified distinctive sets.
 6. A method as claimed in claim 5, wherein the ADR method further comprises: continuous updating of the current transfer function based on an analysis of the parts of the received OFDM signal corresponding to the known parts of the transmitted OFDM signal.
 7. A method as claimed in claim 5, wherein the ADR method further comprises: continuous updating of the current transfer function based on an analysis of the received OFDM signal, in order to accommodate a gradual fading of the received OFDM signal.
 8. A method as claimed in claim 5, wherein the defining distinctive sets of the parameters of the tone signals comprises: using a known pattern of the coding of the data symbols and the current transfer function of the transmission link.
 9. A method for adaptive data recovery (ADR) from a received orthogonal frequency division multiplexing (OFDM) signal by an adaptive decoding of data symbols from parameters of cycles or half-cycles of tone signals of the received OFDM signal, wherein the received OFDM signal is produced by a coding circuit implementing a coding of the data symbols into a transmitted OFDM signal and a transmission link altering the transmitted OFDM signal into the received OFDM signal in accordance to a current transfer function; wherein the method for ADR comprises the steps of: producing, using a received signal processor, the parameters of the cycles or half-cycles of the tone signals from the received OFDM signal wherein the produced parameters correspond to amplitudes or phases of the cycles or half-cycles of the tone signals; deriving the current transfer function by utilizing parts of the received OFDM signal corresponding to known parts of the transmitted OFDM signal; wherein the current transfer function includes dynamic distortions introduced to the received OFDM signal by time variant changes of characteristics of the transmission link; defining distinctive sets of the parameters of the cycles or half-cycles of the tone signals; wherein each of the distinctive sets comprises the parameters of the cycles or half-cycles of the tone signals expected to represent one of the data symbols; preloading the data symbols to a content addressed memory; identifying ones of the distinctive sets comprising the produced parameters by processing the produced parameters; decoding adaptively the data symbols by reversing both the coding of the data symbols and the current transfer function, wherein the reversing of both the coding of the data symbols and the current transfer function is achieved by reading the data symbols from the content addressed memory addressed with identifiers of the identified distinctive sets.
 10. A method as claimed in claim 9, wherein the ADR method further comprises: continuous updating of the current transfer function based on an analysis of the parts of the received OFDM signal corresponding to the known parts of the transmitted OFDM signal.
 11. A method as claimed in claim 9, wherein the ADR method further comprises: continuous updating of the current transfer function based on an analysis of the received OFDM signal, in order to accommodate a gradual fading of the received OFDM signal.
 12. A method as claimed in claim 9, wherein the defining distinctive sets of the cycles or half-cycles of the parameters of the tone signals comprises: using a known pattern of the coding of the data symbols and the current transfer function of the transmission link. 